{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"awesome-harness","slug":"harness","name":"Harness","type":"mcp","url":"https://github.com/harness/mcp-server","page_url":"https://unfragile.ai/harness","categories":["mcp-servers"],"tags":[],"pricing":{"model":"open_source","free":true,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"awesome-harness__cap_0","uri":"capability://tool.use.integration.mcp.standardized.harness.api.bridging.via.json.rpc.stdio.protocol","name":"mcp-standardized harness api bridging via json-rpc stdio protocol","description":"Exposes Harness platform APIs through a Model Context Protocol (MCP) server that communicates with clients (Claude Desktop, VS Code, Cursor, Windsurf) using JSON-RPC 2.0 over stdio. The server acts as a protocol adapter, translating MCP tool calls into authenticated HTTP requests to Harness backend services and marshaling responses back through the MCP interface. This enables AI assistants and development tools to invoke Harness operations without direct API knowledge.","intents":["I want Claude or VS Code to directly interact with my Harness pipelines and repositories without leaving my editor","I need to expose Harness APIs to AI assistants through a standardized protocol that multiple tools already support","I want to build AI-driven workflows that orchestrate Harness operations without writing custom API integration code"],"best_for":["Teams using Claude Desktop, VS Code, Cursor, or Windsurf who want native Harness integration","Organizations building AI agents that need standardized access to CI/CD and deployment platforms","Developers migrating from REST API clients to MCP-compatible tooling"],"limitations":["MCP protocol overhead adds ~50-100ms per request due to JSON-RPC serialization and stdio communication","Client must support MCP protocol — older IDE versions or custom tools require MCP implementation","No persistent connection pooling — each tool invocation establishes new HTTP connection to Harness backend","Limited to tools exposed via MCP toolset registration — not all Harness APIs are automatically available"],"requires":["Go 1.23 or later for building from source","MCP-compatible client (Claude Desktop, VS Code 1.80+, Cursor, Windsurf)","Harness API key or JWT token depending on operational mode","Network access to Harness platform (app.harness.io or internal service URLs)"],"input_types":["JSON-RPC 2.0 method calls with tool parameters","Structured tool arguments (strings, numbers, booleans, objects)"],"output_types":["JSON-RPC 2.0 responses with tool results","Structured data (pipeline runs, artifacts, logs, metrics)"],"categories":["tool-use-integration","mcp-protocol"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-harness__cap_1","uri":"capability://tool.use.integration.dual.mode.authentication.with.api.key.and.jwt.token.providers","name":"dual-mode authentication with api key and jwt token providers","description":"The server implements two distinct authentication mechanisms selected via config.Internal flag: external stdio mode uses APIKeyProvider to authenticate requests with Harness API keys passed by clients, while internal mode uses JWTProvider to authenticate with JWT tokens signed using service-specific secrets. Each provider wraps HTTP client operations, injecting credentials into request headers before forwarding to Harness backend services. This architecture enables the same MCP server to serve both external developers and internal microservices with appropriate security boundaries.","intents":["I want external developers to authenticate with their Harness API keys without exposing internal service credentials","I need internal Harness services to authenticate using JWT tokens with service-specific secrets for audit and isolation","I want to run a single MCP server binary that can operate in both external and internal deployment contexts"],"best_for":["Organizations with both external developer tools and internal microservice architectures","Teams requiring audit trails and service-specific credential isolation","Harness platform operators managing multi-tenant or hybrid deployment scenarios"],"limitations":["API key mode requires clients to manage and pass credentials — no built-in credential rotation or expiration enforcement","JWT mode requires pre-shared service secrets — compromised secrets affect all internal services using that secret","No credential caching or refresh token support — each request re-authenticates, adding latency","Mode is immutable at server startup — cannot switch between API key and JWT authentication without restart"],"requires":["Harness API key (for stdio mode) or JWT token with service-specific secret (for internal mode)","Configuration flag config.Internal set appropriately for deployment context","Network access to Harness platform with appropriate firewall rules for each mode"],"input_types":["API key string (stdio mode)","JWT token string (internal mode)","HTTP request headers"],"output_types":["Authenticated HTTP requests with Authorization headers","Success/failure authentication status"],"categories":["tool-use-integration","safety-moderation"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-harness__cap_10","uri":"capability://tool.use.integration.internal.ai.services.access.with.genai.and.chatbot.integration.internal.mode.only","name":"internal ai services access with genai and chatbot integration (internal mode only)","description":"Exposes internal Harness AI services through AIServices toolset available only in internal mode (JWT authentication). This includes genai service for AI-powered code generation and analysis, and chatbot service for conversational AI interactions. The implementation provides internal Harness microservices with direct access to AI capabilities through MCP tools, enabling AI-driven features within the Harness platform itself. These toolsets are not exposed in external stdio mode for security and licensing reasons.","intents":["I want internal Harness services to leverage AI capabilities for code generation and analysis","I need to integrate conversational AI into Harness platform features","I want to build AI-powered features within the Harness platform using internal AI services"],"best_for":["Harness internal development teams building AI-powered platform features","Internal microservices requiring AI capabilities for code generation or analysis","Harness platform teams implementing conversational AI features"],"limitations":["Only available in internal mode with JWT authentication — not accessible to external clients","Requires service-specific JWT secrets — compromised secrets affect all internal services","No rate limiting or quota management — internal services can exhaust AI service capacity","Limited documentation for internal APIs — requires Harness internal knowledge"],"requires":["Internal mode deployment (config.Internal = true)","JWT token with service-specific secret","Access to internal Harness service URLs"],"input_types":["Code snippets (for genai service)","Prompts (for chatbot service)","Context objects (service-specific)"],"output_types":["Generated code (for genai service)","Analysis results (for genai service)","Conversational responses (for chatbot service)"],"categories":["tool-use-integration","text-generation-language"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-harness__cap_11","uri":"capability://tool.use.integration.connector.management.and.configuration.querying","name":"connector management and configuration querying","description":"Exposes connector operations through a Connectors toolset that enables listing configured connectors, retrieving connector details, validating connector connectivity, and managing connector configurations. The implementation provides access to all Harness connector types (Git, artifact registry, cloud, infrastructure) through unified APIs. This enables AI agents to discover available integrations, validate connector health, and manage connector configurations programmatically.","intents":["I want to list all configured connectors and check their connectivity status","I need to validate that a specific connector is working before using it in a deployment","I want to build an AI agent that manages connector configurations and troubleshoots connectivity issues"],"best_for":["Teams managing multiple Harness connectors across different platforms","DevOps teams automating connector validation and troubleshooting","Organizations building AI-driven connector management workflows"],"limitations":["Connector validation depends on target system availability — validation may fail if targets are unreachable","Connector configuration changes require appropriate permissions — not all users can modify connectors","No built-in connector health monitoring — clients must implement periodic validation","Connector secrets are not exposed through APIs — clients cannot retrieve or modify credentials"],"requires":["Harness API key or JWT token with Connector Service access","Appropriate permissions for connector operations"],"input_types":["Connector type filter (git, artifact, cloud, infrastructure)","Connector identifier (string)","Connector configuration (JSON object)"],"output_types":["Connector list (names, types, status)","Connector details (configuration, credentials status, last validation)","Validation results (success/failure, error messages)","Connector health status (connected, disconnected, error)"],"categories":["tool-use-integration","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-harness__cap_12","uri":"capability://search.retrieval.dashboard.and.metric.visualization.querying.with.custom.dashboard.support","name":"dashboard and metric visualization querying with custom dashboard support","description":"Exposes dashboard operations through a Dashboards toolset that enables listing dashboards, retrieving dashboard definitions, querying dashboard metrics, and analyzing dashboard data. The implementation provides access to Harness dashboards and custom dashboards, enabling AI agents to retrieve metrics and visualizations for analysis. This enables AI agents to understand system state through dashboard data, generate reports, and provide insights based on dashboard metrics.","intents":["I want to retrieve metrics from a specific dashboard to analyze system performance","I need to generate a report based on dashboard data for stakeholder communication","I want to build an AI agent that analyzes dashboard metrics and provides performance insights"],"best_for":["Teams using Harness dashboards for system monitoring and visualization","Organizations building AI-driven reporting and analytics workflows","Teams automating dashboard data retrieval for analysis"],"limitations":["Dashboard data availability depends on configured data sources — missing sources cause incomplete data","Dashboard metric queries may have latency — real-time metrics may not be available","Custom dashboard support depends on dashboard definition format — complex dashboards may not be fully supported","No built-in metric aggregation — clients must implement cross-dashboard analysis"],"requires":["Harness API key or JWT token with Dashboard Service access","Configured dashboards in Harness","Valid dashboard identifier"],"input_types":["Dashboard identifier (string)","Time range (start timestamp, end timestamp)","Metric filter (metric name, dimension)"],"output_types":["Dashboard list (names, types, descriptions)","Dashboard definition (layout, panels, metrics)","Dashboard metrics (values, timestamps, dimensions)","Dashboard analysis (trends, anomalies, insights)"],"categories":["search-retrieval","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-harness__cap_13","uri":"capability://safety.moderation.read.only.mode.enforcement.with.configurable.write.operation.restrictions","name":"read-only mode enforcement with configurable write operation restrictions","description":"Implements a read-only mode that can be enabled via --read-only flag in stdio mode, preventing write operations (pipeline execution, PR comments, connector modifications) while allowing read operations (querying status, retrieving logs, listing resources). The implementation enforces read-only restrictions at the toolset level by conditionally registering write-capable tools. This enables safe deployment of MCP servers in restricted environments where only query operations are permitted.","intents":["I want to deploy an MCP server that allows querying Harness data but prevents accidental modifications","I need to provide read-only access to Harness for auditing and analysis without modification risks","I want to run an MCP server in a restricted environment where write operations are not permitted"],"best_for":["Organizations requiring read-only access for auditing and compliance","Teams deploying MCP servers in restricted environments","Developers building analysis-only AI agents without modification capabilities"],"limitations":["Read-only mode is enforced at startup — cannot be changed without server restart","Write operation prevention is at toolset level — no fine-grained per-operation control","Internal mode always enforces read-only — cannot be overridden for internal services","No audit logging of prevented write attempts — clients don't receive feedback on blocked operations"],"requires":["--read-only flag passed to server startup","Harness API key or JWT token with appropriate permissions"],"input_types":["Command-line flag: --read-only"],"output_types":["Read-only mode status (enabled/disabled)","Available tools (filtered for read-only operations)"],"categories":["safety-moderation","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-harness__cap_2","uri":"capability://tool.use.integration.layered.toolset.registration.with.service.client.abstraction","name":"layered toolset registration with service client abstraction","description":"The server uses a layered architecture where InitToolsets function orchestrates the registration of multiple domain-specific toolsets (Pipeline, PullRequest, Repository, ArtifactRegistry, CloudCost, ChaosEngineering, Logs, AIServices, Connectors, Dashboards). Each toolset follows a consistent registration pattern: create an HTTP client with appropriate authentication, instantiate a service client that wraps Harness API operations, create a toolset with individual tools, and add it to a toolset group. Service clients abstract HTTP details and provide business logic, while toolsets expose individual operations as MCP tools with standardized parameter schemas.","intents":["I want to organize Harness operations into logical domains (pipelines, repositories, artifacts) that map to my mental model","I need to add new Harness capabilities without modifying core MCP protocol handling code","I want consistent parameter validation and response formatting across all Harness operations"],"best_for":["Teams extending Harness MCP server with new toolsets or operations","Organizations with large Harness deployments spanning multiple functional domains","Developers building domain-specific AI agents that need organized access to Harness features"],"limitations":["Toolset registration is static at server startup — cannot dynamically add/remove toolsets at runtime","Each toolset creates separate HTTP client and service client instances — adds memory overhead for large deployments","Service client abstraction adds ~10-20ms per request due to additional function call layers","No built-in dependency management between toolsets — circular dependencies or ordering issues must be handled manually"],"requires":["Go 1.23+ for building custom toolsets","Understanding of MCP tool schema format and Harness API patterns","HTTP client configuration with appropriate authentication provider"],"input_types":["Toolset configuration objects","Service client instances","Tool definitions with parameter schemas"],"output_types":["Registered toolset groups","MCP tool definitions with JSON schemas","Tool execution results"],"categories":["tool-use-integration","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-harness__cap_3","uri":"capability://automation.workflow.pipeline.execution.and.status.monitoring.with.real.time.log.streaming","name":"pipeline execution and status monitoring with real-time log streaming","description":"Exposes Harness pipeline operations through a Pipeline toolset that enables triggering pipeline executions, querying execution status, retrieving execution logs, and monitoring execution stages. The implementation wraps Harness Pipeline Service APIs, allowing clients to start pipelines with input variables, poll execution status with stage-level granularity, and stream execution logs in real-time. This enables AI agents to orchestrate CI/CD workflows and provide developers with execution feedback without manual dashboard navigation.","intents":["I want to trigger a Harness pipeline from Claude and get real-time feedback on execution progress","I need to retrieve logs from a specific pipeline execution stage to debug failures","I want to build an AI agent that monitors pipeline status and takes corrective actions based on stage failures"],"best_for":["Teams using Harness for CI/CD who want AI-driven pipeline orchestration","Developers debugging pipeline failures through AI assistants","Organizations building self-healing CI/CD systems with automated remediation"],"limitations":["Log streaming is pull-based (polling) rather than push-based — adds latency for real-time monitoring","Pipeline execution parameters must match Harness variable schema — type mismatches cause execution failures","No built-in retry logic for failed executions — clients must implement retry strategies","Stage-level granularity is limited to Harness pipeline definition — cannot monitor sub-stage operations"],"requires":["Harness API key or JWT token with Pipeline Service access","Valid Harness project and pipeline identifiers","Pipeline definition with input variables if passing execution parameters"],"input_types":["Pipeline identifier (string)","Execution input variables (JSON object)","Stage names (string)"],"output_types":["Execution ID (string)","Execution status (RUNNING, SUCCESS, FAILED, etc.)","Stage execution details (status, duration, logs)","Log lines (text)"],"categories":["automation-workflow","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-harness__cap_4","uri":"capability://tool.use.integration.pull.request.and.code.review.integration.with.repository.context","name":"pull request and code review integration with repository context","description":"Exposes pull request operations through a PullRequest toolset that enables querying pull requests, retrieving PR details with diff context, posting comments, and managing PR status. The implementation integrates with Harness repository connectors (GitHub, GitLab, Bitbucket) to fetch PR metadata and code changes. This enables AI agents to analyze code changes, provide automated code review feedback, and participate in pull request discussions without leaving the MCP client.","intents":["I want Claude to analyze a pull request and provide code review feedback directly in the PR","I need to retrieve the diff for a specific PR to understand what changed before running tests","I want to build an AI code reviewer that automatically comments on PRs with suggestions"],"best_for":["Teams using Harness with GitHub/GitLab/Bitbucket connectors for code review automation","Organizations implementing AI-assisted code review workflows","Developers integrating AI analysis into pull request workflows"],"limitations":["PR operations depend on configured repository connectors — missing connectors cause failures","Diff retrieval is limited to Harness connector capabilities — some Git platforms may have incomplete diff support","Comment posting requires appropriate permissions in the connected Git platform","No built-in conflict resolution for concurrent PR comments — clients must handle comment ordering"],"requires":["Harness API key or JWT token with Repository Service access","Configured repository connector (GitHub, GitLab, Bitbucket) in Harness","Valid repository and PR identifiers"],"input_types":["Repository identifier (string)","Pull request number (integer)","Comment text (string)","PR status (OPEN, CLOSED, MERGED)"],"output_types":["PR metadata (title, author, status, created date)","PR diff (unified diff format)","Comments (author, text, timestamp)","Review status (APPROVED, CHANGES_REQUESTED, COMMENTED)"],"categories":["tool-use-integration","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-harness__cap_5","uri":"capability://search.retrieval.repository.browsing.and.file.retrieval.with.connector.abstraction","name":"repository browsing and file retrieval with connector abstraction","description":"Exposes repository operations through a Repository toolset that enables listing repositories, browsing directory structures, retrieving file contents, and querying commit history. The implementation uses Harness repository connectors to abstract Git platform differences, allowing clients to browse code without direct Git API knowledge. This enables AI agents to analyze codebases, retrieve configuration files, and understand repository structure for context-aware operations.","intents":["I want Claude to browse my repository structure and retrieve specific files for analysis","I need to get the commit history for a file to understand its evolution","I want to build an AI agent that analyzes repository configuration files to make deployment decisions"],"best_for":["Teams using Harness with Git connectors who want AI-driven codebase analysis","Organizations building AI agents that need repository context for decision-making","Developers using AI assistants to understand unfamiliar codebases"],"limitations":["Repository browsing performance depends on Git connector implementation — large repositories may timeout","File size limits are enforced by Harness connectors — binary files and large files may not be retrievable","Commit history is limited to configured branch — cross-branch history requires separate queries","No built-in caching — repeated file retrievals cause redundant API calls to Git platforms"],"requires":["Harness API key or JWT token with Repository Service access","Configured repository connector in Harness","Valid repository identifier and branch name"],"input_types":["Repository identifier (string)","Branch name (string)","File path (string)","Directory path (string)"],"output_types":["Repository list (names, URLs, connectors)","Directory listing (files, subdirectories, file types)","File contents (text or binary)","Commit history (author, message, timestamp, hash)"],"categories":["search-retrieval","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-harness__cap_6","uri":"capability://search.retrieval.artifact.registry.querying.and.artifact.retrieval.with.multi.registry.support","name":"artifact registry querying and artifact retrieval with multi-registry support","description":"Exposes artifact operations through an ArtifactRegistry toolset that enables querying artifact registries (Docker, Artifactory, ECR, GCR, Nexus), listing artifacts, retrieving artifact metadata, and downloading artifacts. The implementation uses Harness artifact connector abstraction to support multiple registry types with unified APIs. This enables AI agents to discover available artifacts, retrieve artifact information for deployment decisions, and automate artifact-driven workflows.","intents":["I want to query available Docker images in my artifact registry and get metadata for the latest version","I need to retrieve artifact information to make deployment decisions based on artifact properties","I want to build an AI agent that automatically selects and deploys the appropriate artifact version"],"best_for":["Teams using Harness with artifact connectors for artifact-driven deployments","Organizations building AI-driven artifact selection and deployment workflows","DevOps teams automating artifact discovery and validation"],"limitations":["Artifact registry operations depend on configured connectors — missing connectors cause failures","Query performance varies by registry type — large registries may have slow list operations","Artifact metadata availability depends on registry type — some registries provide limited metadata","No built-in artifact validation — clients must implement version compatibility checks"],"requires":["Harness API key or JWT token with Artifact Registry Service access","Configured artifact connector (Docker, Artifactory, ECR, GCR, Nexus) in Harness","Valid artifact repository identifier"],"input_types":["Registry type (string: docker, artifactory, ecr, gcr, nexus)","Repository name (string)","Artifact name (string)","Version/tag (string)"],"output_types":["Artifact list (names, versions, tags)","Artifact metadata (size, created date, digest, layers)","Registry information (type, URL, credentials status)","Artifact binary (for download operations)"],"categories":["search-retrieval","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-harness__cap_7","uri":"capability://data.processing.analysis.cloud.cost.analysis.and.optimization.recommendations.with.multi.cloud.support","name":"cloud cost analysis and optimization recommendations with multi-cloud support","description":"Exposes cloud cost operations through a CloudCost toolset that enables querying cloud spending, analyzing cost trends, retrieving cost anomalies, and generating optimization recommendations. The implementation integrates with Harness Cloud Cost Management service to aggregate costs across AWS, Azure, and GCP, providing unified cost visibility and AI-driven optimization insights. This enables AI agents to analyze spending patterns, identify cost anomalies, and recommend resource optimization strategies.","intents":["I want Claude to analyze my cloud spending trends and identify cost anomalies","I need optimization recommendations for my cloud infrastructure to reduce costs","I want to build an AI agent that monitors cloud spending and alerts on unusual patterns"],"best_for":["Organizations using Harness Cloud Cost Management for multi-cloud cost visibility","FinOps teams automating cost analysis and optimization workflows","Teams building AI-driven cost optimization agents"],"limitations":["Cost data has latency — real-time cost information is not available, typically 24-48 hour delay","Optimization recommendations are based on historical patterns — may not account for planned infrastructure changes","Multi-cloud aggregation depends on configured cloud connectors — missing connectors cause incomplete cost data","Cost anomaly detection uses statistical models — may produce false positives in highly variable workloads"],"requires":["Harness API key or JWT token with Cloud Cost Management Service access","Configured cloud connectors (AWS, Azure, GCP) in Harness","Cloud Cost Management license/feature enabled"],"input_types":["Time range (start date, end date)","Cloud provider filter (AWS, Azure, GCP)","Cost category filter (compute, storage, network, etc.)","Anomaly threshold (percentage or absolute value)"],"output_types":["Cost summary (total, by provider, by category)","Cost trends (daily/weekly/monthly breakdown)","Cost anomalies (detected unusual spending)","Optimization recommendations (action, estimated savings, priority)"],"categories":["data-processing-analysis","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-harness__cap_8","uri":"capability://automation.workflow.chaos.engineering.experiment.execution.and.result.analysis","name":"chaos engineering experiment execution and result analysis","description":"Exposes chaos engineering operations through a ChaosEngineering toolset that enables creating chaos experiments, executing experiments, monitoring experiment progress, and analyzing experiment results. The implementation integrates with Harness Chaos Engineering service to define fault injection scenarios, execute them against target systems, and collect metrics on system resilience. This enables AI agents to orchestrate chaos testing workflows, analyze resilience patterns, and recommend system hardening strategies.","intents":["I want to execute a chaos experiment against my microservices and analyze the results","I need to understand how my system behaves under failure conditions to improve resilience","I want to build an AI agent that automatically runs chaos experiments and recommends system improvements"],"best_for":["Organizations using Harness Chaos Engineering for resilience testing","SRE teams automating chaos testing and resilience analysis","Teams building AI-driven system resilience improvement workflows"],"limitations":["Chaos experiments require target system access — experiments may fail if targets are unreachable","Experiment results depend on system state — same experiment may produce different results on different runs","Experiment execution is asynchronous — clients must poll for completion status","No built-in rollback mechanism — failed experiments may leave systems in degraded state"],"requires":["Harness API key or JWT token with Chaos Engineering Service access","Configured target systems and Kubernetes clusters for chaos experiments","Chaos Engineering license/feature enabled"],"input_types":["Experiment definition (fault type, target, duration, parameters)","Target system identifier (service, pod, node)","Fault parameters (latency, error rate, resource limits)"],"output_types":["Experiment ID (string)","Experiment status (RUNNING, COMPLETED, FAILED)","Experiment results (metrics, logs, analysis)","System behavior metrics (latency, error rate, resource usage)"],"categories":["automation-workflow","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-harness__cap_9","uri":"capability://search.retrieval.log.aggregation.and.analysis.with.multi.source.querying","name":"log aggregation and analysis with multi-source querying","description":"Exposes log operations through a Logs toolset that enables querying logs from multiple sources (application logs, infrastructure logs, deployment logs), filtering by time range and criteria, and analyzing log patterns. The implementation integrates with Harness Logs service to aggregate logs from various sources and provide unified querying and analysis capabilities. This enables AI agents to investigate issues, analyze error patterns, and correlate logs across multiple systems.","intents":["I want to query logs from a specific time range to investigate a production issue","I need to analyze error patterns in my application logs to identify root causes","I want to build an AI agent that automatically analyzes logs and provides incident summaries"],"best_for":["Teams using Harness for log aggregation and analysis","SRE and DevOps teams automating incident investigation","Organizations building AI-driven log analysis and alerting workflows"],"limitations":["Log query performance depends on log volume — large time ranges may timeout","Log retention is limited by Harness configuration — historical logs may not be available","Log filtering depends on log structure — unstructured logs may be difficult to query","No built-in log correlation across services — clients must implement correlation logic"],"requires":["Harness API key or JWT token with Logs Service access","Configured log sources in Harness","Valid service and time range for log queries"],"input_types":["Service identifier (string)","Time range (start timestamp, end timestamp)","Filter criteria (log level, keyword, source)","Query string (log search syntax)"],"output_types":["Log entries (timestamp, level, message, source)","Log statistics (count by level, by source)","Log patterns (frequent error messages, anomalies)","Log analysis results (root cause suggestions, correlations)"],"categories":["search-retrieval","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":32,"verified":false,"data_access_risk":"high","permissions":["Go 1.23 or later for building from source","MCP-compatible client (Claude Desktop, VS Code 1.80+, Cursor, Windsurf)","Harness API key or JWT token depending on operational mode","Network access to Harness platform (app.harness.io or internal service URLs)","Harness API key (for stdio mode) or JWT token with service-specific secret (for internal mode)","Configuration flag config.Internal set appropriately for deployment context","Network access to Harness platform with appropriate firewall rules for each mode","Internal mode deployment (config.Internal = true)","JWT token with service-specific secret","Access to internal Harness service URLs"],"failure_modes":["MCP protocol overhead adds ~50-100ms per request due to JSON-RPC serialization and stdio communication","Client must support MCP protocol — older IDE versions or custom tools require MCP implementation","No persistent connection pooling — each tool invocation establishes new HTTP connection to Harness backend","Limited to tools exposed via MCP toolset registration — not all Harness APIs are automatically available","API key mode requires clients to manage and pass credentials — no built-in credential rotation or expiration enforcement","JWT mode requires pre-shared service secrets — compromised secrets affect all internal services using that secret","No credential caching or refresh token support — each request re-authenticates, adding latency","Mode is immutable at server startup — cannot switch between API key and JWT authentication without restart","Only available in internal mode with JWT authentication — not accessible to external clients","Requires service-specific JWT secrets — compromised secrets affect all internal services","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.05,"quality":0.5,"ecosystem":0.39999999999999997,"match_graph":0.25,"freshness":0.52,"weights":{"adoption":0.25,"quality":0.25,"ecosystem":0.15,"match_graph":0.23,"freshness":0.12}},"observed_outcomes":{"matches":0,"success_rate":0,"avg_confidence":0,"top_intents":[],"last_matched_at":null},"maintenance":{"status":"active","updated_at":"2026-06-17T09:51:03.041Z","last_scraped_at":"2026-05-03T14:00:15.503Z","last_commit":null},"community":{"stars":null,"forks":null,"weekly_downloads":null,"model_downloads":null,"model_likes":null}},"distribution":{"claim_url":"https://unfragile.ai/submit?claim=harness","compare_url":"https://unfragile.ai/compare?artifact=harness"}},"signature":"OU36rupdsgC2vuUBLW9jQVg84mznKpLtrXj8xQiBKc541P8mOlGLeUPjLkTSDWMd9otQZ2PVDZ3cyPMl/8oQBQ==","signedAt":"2026-06-23T03:01:27.202Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/harness","artifact":"https://unfragile.ai/harness","verify":"https://unfragile.ai/api/v1/verify?slug=harness","publicKey":"https://unfragile.ai/api/v1/trust-passport-public-key","spec":"https://unfragile.ai/trust","schema":"https://unfragile.ai/schema.json","docs":"https://unfragile.ai/docs"}}