{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"github_mcp-accenture-mcp-bench","slug":"mcp-accenture-mcp-bench","name":"mcp-bench","type":"mcp","url":"https://github.com/Accenture/mcp-bench","page_url":"https://unfragile.ai/mcp-accenture-mcp-bench","categories":["mcp-servers"],"tags":[],"pricing":{"model":"open_source","free":true,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"github_mcp-accenture-mcp-bench__cap_0","uri":"capability://planning.reasoning.multi.server.tool.use.benchmarking.with.complexity.stratification","name":"multi-server tool-use benchmarking with complexity stratification","description":"Evaluates LLM agents across three task complexity tiers (single-server, two-server, three-server) by orchestrating tool discovery, selection, and execution across 28 diverse MCP servers. The framework uses a task execution pipeline that manages persistent MCP server connections via connection pooling, routes tool calls through a schema-aware dispatcher, and measures success via multi-dimensional metrics combining LLM-as-judge scoring with rule-based compliance checks.","intents":["Compare how different LLMs handle tool coordination across multiple independent service providers","Measure LLM agent planning effectiveness when tools must be chained across domain boundaries","Identify failure modes in tool selection when schema complexity increases across server tiers","Benchmark real-world task completion rates for agents using biomedical, financial, and academic tools simultaneously"],"best_for":["LLM capability researchers evaluating tool-use performance across model families","Teams building production LLM agents who need baseline metrics for multi-tool orchestration","MCP server developers validating tool discoverability and schema clarity"],"limitations":["Evaluation latency scales with task complexity — three-server tasks require sequential MCP round-trips, adding 2-5 seconds per agent step","LLM-as-judge scoring introduces variance dependent on judge model quality; results not deterministic across runs","Rate limiting per MCP server can cause task timeouts if concurrent execution exceeds server-specific thresholds","Benchmark results are snapshot-based; no continuous regression detection across model versions"],"requires":["Python 3.9+","API credentials for at least one LLM provider (Azure OpenAI, OpenRouter, or OpenAI-compatible endpoint)","28 MCP servers installed via mcp_servers/install.sh with working subprocess execution","Network connectivity to external services (Google Maps, cryptocurrency exchanges, academic APIs)"],"input_types":["task definitions (JSON with goal, required_servers, expected_tools)","LLM configuration (model name, provider, temperature, max_tokens)","MCP server manifests (tool schemas, rate limits, authentication)"],"output_types":["structured benchmark results (JSON with task_completion_rate, tool_usage_metrics, planning_scores)","LLM-as-judge evaluations (scoring rationale, compliance violations)","execution traces (tool calls, latencies, error logs per server)"],"categories":["planning-reasoning","tool-use-integration","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github_mcp-accenture-mcp-bench__cap_1","uri":"capability://tool.use.integration.persistent.mcp.server.connection.pooling.with.concurrent.tool.execution","name":"persistent mcp server connection pooling with concurrent tool execution","description":"Manages long-lived connections to 28 MCP servers using connection pooling (via ServerManagerPersistent) to avoid subprocess spawn overhead per tool call. Executes tool invocations concurrently with server-specific rate limiting and timeout enforcement, routing calls through a schema-aware dispatcher that validates tool parameters against declared MCP schemas before execution.","intents":["Reduce latency overhead when agents make multiple sequential tool calls to the same MCP server","Prevent rate-limit violations by enforcing per-server concurrency caps and request throttling","Ensure tool calls conform to declared schemas before transmission, catching malformed requests early","Execute independent tool calls in parallel across different servers to speed up multi-server tasks"],"best_for":["Benchmark runners executing hundreds of tasks requiring repeated tool calls","Production LLM agents where connection reuse reduces per-call latency by 50-70%","Teams integrating MCP servers with strict rate limits (e.g., paid APIs)"],"limitations":["Connection pooling adds ~100ms overhead on first connection per server; subsequent calls reuse connections","Rate limiting is enforced per-server but not globally — concurrent tasks may still exceed aggregate API quotas","Timeout enforcement is process-level; hung MCP server processes may block connection pool until timeout expires (default 30s)","No automatic reconnection on transient network failures; requires manual retry logic in caller"],"requires":["Python 3.9+ with asyncio support","MCP servers installed and executable via subprocess (mcp_servers/install.sh)","Server configuration in mcp_servers/commands.json with rate_limit_per_minute and timeout_seconds fields"],"input_types":["tool invocation requests (tool_name, parameters as JSON)","server configuration (command, rate_limit, timeout)"],"output_types":["tool execution results (JSON response from MCP server)","execution metadata (latency_ms, server_name, rate_limit_remaining)"],"categories":["tool-use-integration","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github_mcp-accenture-mcp-bench__cap_10","uri":"capability://tool.use.integration.28.server.mcp.ecosystem.with.domain.specific.tool.coverage","name":"28-server mcp ecosystem with domain-specific tool coverage","description":"Provides a curated ecosystem of 28 MCP servers spanning biomedical (BioMCP, Medical Calculator), location services (Google Maps, National Parks), academic research (Call for Papers, Paper Search, Wikipedia), finance (DEX Paprika, OKX Exchange), technology (Hugging Face, NixOS, OpenAPI Explorer), data science (NASA Data, Scientific Computing, Weather), and entertainment (Movie Recommender, Game Trends, Reddit). Each server is pre-configured with tool schemas, rate limits, and authentication, enabling agents to discover and use domain-specific tools.","intents":["Evaluate agent performance across diverse real-world domains (biomedical, finance, academic, entertainment)","Test tool discovery and selection when agents have access to 100+ tools across different domains","Benchmark cross-domain coordination (e.g., combining financial data with academic research)","Provide realistic tool ecosystems for production-like agent evaluation"],"best_for":["Researchers evaluating agent generalization across diverse domains","Teams building production agents that need realistic tool ecosystems","Benchmark designers creating comprehensive evaluation suites"],"limitations":["Server availability depends on external service uptime (e.g., Google Maps API, cryptocurrency exchanges)","Authentication requirements vary per server (API keys, OAuth tokens) — setup complexity increases with server count","Some servers have usage quotas or rate limits that may be exceeded during large-scale benchmarking","Server tool sets are static; no support for custom or proprietary tools without extending the framework"],"requires":["API credentials for external services (Google Maps API key, cryptocurrency exchange API keys, etc.)","Network connectivity to all 28 external services","MCP server installation via mcp_servers/install.sh","Configuration in mcp_servers/commands.json with server commands and rate limits"],"input_types":["server configuration (command, rate_limit_per_minute, timeout_seconds, authentication)"],"output_types":["tool registry (28 servers with 100+ tools)","tool metadata (name, description, parameters, required_fields)"],"categories":["tool-use-integration","memory-knowledge"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github_mcp-accenture-mcp-bench__cap_11","uri":"capability://planning.reasoning.agent.planning.and.reasoning.with.multi.turn.tool.coordination","name":"agent planning and reasoning with multi-turn tool coordination","description":"Implements agent reasoning loops that discover available tools, plan tool sequences to achieve task goals, execute tools, observe results, and adapt plans based on outcomes. Agents maintain conversation history with the LLM, enabling multi-turn reasoning where each tool result informs subsequent planning steps. The executor (agent/executor.py) orchestrates these loops, managing tool invocations, error handling, and termination conditions (max steps, task completion).","intents":["Enable agents to plan complex multi-step workflows across multiple tools","Support adaptive planning where agents adjust strategies based on tool results","Measure planning quality by analyzing tool sequences and reasoning steps","Debug agent failures by examining planning decisions and tool selections"],"best_for":["Researchers studying LLM planning capabilities in tool-use scenarios","Teams building production agents requiring multi-step reasoning","Benchmark runners evaluating planning quality across LLM models"],"limitations":["Planning quality depends heavily on LLM model capability — weaker models produce incoherent plans","Multi-turn reasoning increases latency (1-3 seconds per turn) due to LLM round-trips","No built-in plan validation — agents may generate infeasible plans (e.g., using non-existent tools)","Error recovery is reactive (observe error, replan) rather than proactive (validate plan before execution)"],"requires":["LLM provider with function calling support (OpenAI, Azure OpenAI, OpenRouter)","Tool schemas for agent planning (from schema registry)","Execution environment with MCP servers for tool invocation"],"input_types":["task goal (natural language description of desired outcome)","available tools (tool names, descriptions, parameter schemas)","tool results (JSON responses from previous tool invocations)"],"output_types":["agent plan (sequence of tool calls with parameters)","reasoning steps (natural language explanation of planning decisions)","final result (task completion status, outcome)"],"categories":["planning-reasoning","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github_mcp-accenture-mcp-bench__cap_2","uri":"capability://planning.reasoning.llm.as.judge.multi.dimensional.task.evaluation.with.rule.based.compliance.scoring","name":"llm-as-judge multi-dimensional task evaluation with rule-based compliance scoring","description":"Combines LLM-based semantic evaluation (using a judge model to score task completion quality) with rule-based metrics (tool usage patterns, schema compliance, planning effectiveness). The evaluator runs post-execution analysis on agent traces, extracting tool call sequences, measuring planning coherence, and detecting schema violations, then synthesizes scores into a multi-dimensional result set with per-dimension rationale.","intents":["Assess whether agents completed tasks correctly even if tool sequences differ from expected paths","Detect subtle planning failures (e.g., correct tools selected but in wrong order) that binary pass/fail misses","Measure schema compliance violations (e.g., missing required parameters, type mismatches) automatically","Generate human-readable evaluation rationale explaining why a task succeeded or failed"],"best_for":["Researchers comparing LLM planning quality across model families","Teams needing detailed failure analysis beyond simple success metrics","Benchmark runners requiring reproducible evaluation across multiple task runs"],"limitations":["LLM judge introduces variance — same task may receive different scores across runs due to judge model stochasticity","Judge model quality directly impacts evaluation reliability; weak judges may miss real failures or false-positive successes","Evaluation latency is O(n) in task count — each task requires a separate LLM judge call, adding 1-3 seconds per task","Rule-based metrics are domain-agnostic; task-specific success criteria require custom evaluation logic"],"requires":["API credentials for judge LLM (typically same provider as agent LLM)","Execution traces from agent (tool_calls, parameters, results, timestamps)","MCP server schemas for compliance checking (from mcp_servers/commands.json)"],"input_types":["task definition (goal, expected_outcome)","execution trace (JSON with tool_calls, results, agent_reasoning)","server schemas (tool definitions with parameter types and requirements)"],"output_types":["evaluation scores (task_completion: 0-1, planning_effectiveness: 0-1, schema_compliance: 0-1)","dimension-specific rationale (text explanation per score)","violation list (detected schema mismatches, missing tools, incorrect parameters)"],"categories":["planning-reasoning","safety-moderation"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github_mcp-accenture-mcp-bench__cap_3","uri":"capability://tool.use.integration.multi.llm.provider.abstraction.with.unified.agent.interface","name":"multi-llm provider abstraction with unified agent interface","description":"Abstracts LLM provider differences (Azure OpenAI, OpenRouter, OpenAI-compatible) behind a unified LLMFactory that returns provider-agnostic Agent instances. Agents use a consistent message-passing interface for tool discovery, planning, and execution, with provider-specific details (API endpoints, authentication, model names) isolated in configuration. Supports streaming and non-streaming modes, automatic retry with exponential backoff, and token counting for cost tracking.","intents":["Compare agent performance across different LLM providers without rewriting agent logic","Switch between providers (e.g., OpenAI to Azure to OpenRouter) via configuration changes only","Track token usage and costs per provider for benchmarking economics","Implement provider-agnostic agent code that works with any OpenAI-compatible endpoint"],"best_for":["Researchers benchmarking multiple LLM families (GPT-4, Claude, Llama) in parallel","Teams migrating between cloud providers without code refactoring","Cost-conscious builders comparing provider pricing across identical workloads"],"limitations":["Abstraction hides provider-specific capabilities (e.g., vision, function calling variants) — lowest-common-denominator feature set","Token counting is approximate for non-OpenAI providers; actual billing may differ","Retry logic uses fixed exponential backoff; no provider-specific rate limit adaptation","Streaming mode not fully tested across all providers; non-streaming mode is primary code path"],"requires":["Python 3.9+","API credentials for at least one provider (OPENAI_API_KEY, AZURE_OPENAI_KEY, OPENROUTER_API_KEY)","Provider configuration in config.yaml (model_name, api_base, api_version for Azure)"],"input_types":["provider configuration (provider_type, model_name, api_key, endpoint)","agent task (system_prompt, user_message, available_tools)"],"output_types":["agent response (text, tool_calls, reasoning)","usage metadata (input_tokens, output_tokens, cost_usd)"],"categories":["tool-use-integration","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github_mcp-accenture-mcp-bench__cap_4","uri":"capability://automation.workflow.task.driven.benchmark.execution.with.result.persistence.and.reporting","name":"task-driven benchmark execution with result persistence and reporting","description":"Orchestrates end-to-end benchmark runs via BenchmarkRunner, which loads task definitions from YAML, spawns agent instances per task, collects execution traces and evaluation results, and persists results to structured JSON output. Supports batch execution with configurable parallelism, task filtering by complexity tier, and result aggregation with statistical summaries (mean/median/stddev across tasks).","intents":["Execute 100+ benchmark tasks in parallel with automatic result collection and deduplication","Filter benchmark runs by task complexity (single/two/three-server) for targeted evaluation","Generate statistical summaries comparing agent performance across LLM models","Persist detailed execution traces for post-hoc analysis and failure debugging"],"best_for":["Benchmark researchers running large-scale evaluations across multiple LLM models","CI/CD pipelines validating LLM agent performance on model updates","Teams generating public benchmark leaderboards with reproducible results"],"limitations":["Parallelism is limited by MCP server concurrency caps — actual throughput may be lower than configured worker count","Task definitions are static YAML; no dynamic task generation or adaptive sampling","Result persistence is file-based JSON; no database backend for querying across benchmark runs","No built-in result visualization — requires external tools (Jupyter, Plotly) for analysis"],"requires":["Python 3.9+","Task definitions in YAML format (tasks/benchmark_tasks.yaml)","LLM provider credentials for all models in benchmark","Disk space for result JSON (typically 10-50MB per 100 tasks)"],"input_types":["task YAML (task_id, goal, required_servers, expected_tools, complexity_tier)","benchmark configuration (num_workers, timeout_seconds, models_to_test)","LLM provider credentials"],"output_types":["results JSON (per-task scores, execution traces, evaluation rationale)","summary statistics (mean/median/stddev per metric, per model)","execution logs (timestamps, errors, rate limit events)"],"categories":["automation-workflow","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github_mcp-accenture-mcp-bench__cap_5","uri":"capability://tool.use.integration.tool.schema.discovery.and.validation.with.mcp.manifest.introspection","name":"tool schema discovery and validation with mcp manifest introspection","description":"Discovers available tools by introspecting MCP server manifests (from mcp_servers/commands.json), extracting tool names, parameter schemas, descriptions, and required fields. Validates tool invocations against schemas before execution, detecting missing required parameters, type mismatches, and enum violations. Exposes tool metadata to agents via a unified schema registry, enabling agents to reason about tool capabilities and constraints.","intents":["Automatically discover all available tools across 28 MCP servers without manual documentation","Validate tool calls before execution to catch malformed requests early","Enable agents to reason about tool constraints (required fields, parameter types) when planning","Generate human-readable tool descriptions for agent prompts"],"best_for":["Agents that need to discover tools dynamically rather than using hardcoded tool lists","Teams validating tool schema correctness before deploying MCP servers","Benchmark runners ensuring tool calls conform to declared schemas"],"limitations":["Schema discovery is static (read from mcp_servers/commands.json at startup); no runtime schema updates","Validation is schema-level only — does not check semantic correctness (e.g., valid API keys, reachable endpoints)","Tool descriptions are limited to what MCP manifests provide; no augmentation with usage examples","No support for tools with complex nested schemas or recursive parameter types"],"requires":["MCP server manifests in mcp_servers/commands.json with tool definitions","Tool schemas in JSON Schema format (draft 7 or later)","Python 3.9+ with jsonschema library"],"input_types":["MCP manifest JSON (tool_name, description, input_schema)","tool invocation (tool_name, parameters as JSON)"],"output_types":["schema registry (dict mapping tool_name to schema)","validation result (valid: bool, errors: list of violations)","tool metadata (name, description, required_fields, parameter_types)"],"categories":["tool-use-integration","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github_mcp-accenture-mcp-bench__cap_6","uri":"capability://automation.workflow.agent.execution.trace.collection.and.structured.logging","name":"agent execution trace collection and structured logging","description":"Captures detailed execution traces during agent task execution, recording each tool call (name, parameters, result), agent reasoning steps, latencies, and errors. Traces are structured as JSON with timestamps and server metadata, enabling post-hoc analysis of planning quality, tool selection patterns, and failure modes. Supports both streaming (real-time trace output) and batch (post-execution trace collection) modes.","intents":["Debug agent failures by examining the exact sequence of tool calls and reasoning","Analyze planning patterns (e.g., which tools agents select first, how they handle errors)","Measure per-step latencies to identify bottlenecks in tool execution","Generate detailed reports for stakeholders explaining agent behavior"],"best_for":["Researchers analyzing agent planning strategies and failure modes","Teams debugging production agent failures with detailed execution logs","Benchmark runners generating detailed evaluation reports"],"limitations":["Trace collection adds ~5-10% overhead per task due to JSON serialization","Traces can be large (100KB-1MB per task) for complex multi-step executions","No built-in trace compression or archival — requires external storage management","Streaming traces require real-time log aggregation; batch mode simpler but loses real-time visibility"],"requires":["Python 3.9+ with logging module","Disk space for trace JSON files (10-50MB per 100 tasks)","JSON parsing capability for post-hoc analysis"],"input_types":["agent execution events (tool_call, result, reasoning, error)","execution context (task_id, model_name, timestamp)"],"output_types":["structured trace JSON (tool_calls, results, latencies, errors)","trace summary (num_steps, total_latency, error_count)","human-readable trace report (markdown or HTML)"],"categories":["automation-workflow","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github_mcp-accenture-mcp-bench__cap_7","uri":"capability://planning.reasoning.task.complexity.stratification.with.single.two.three.server.tiers","name":"task complexity stratification with single/two/three-server tiers","description":"Organizes benchmark tasks into three complexity tiers based on the number of MCP servers required: single-server (tools from one server), two-server (coordinating tools across two servers), and three-server (managing complex workflows across three servers). Each tier tests different agent capabilities: single-server tests tool selection accuracy, two-server tests cross-domain coordination, three-server tests planning under high complexity. Tasks are tagged with complexity_tier in YAML definitions.","intents":["Isolate and measure specific agent capabilities (tool selection vs. cross-domain coordination vs. complex planning)","Compare agent performance across complexity tiers to identify scaling limitations","Generate separate leaderboards per tier for fair comparison across model families","Design targeted evaluation suites (e.g., focus on two-server tasks for coordination testing)"],"best_for":["Researchers studying how agent planning degrades with task complexity","Teams identifying which LLM models excel at multi-server coordination","Benchmark designers creating progressive difficulty evaluation suites"],"limitations":["Complexity is defined by server count, not task difficulty — two-server tasks may be easier than some single-server tasks","No automatic task generation per tier; all tasks must be manually authored","Tier definitions are fixed (1/2/3 servers); no support for 4+ server tasks","No adaptive difficulty — task difficulty within a tier varies widely"],"requires":["Task definitions in YAML with complexity_tier field (single_server, two_server, three_server)","MCP servers installed for all required_servers in each task"],"input_types":["task YAML with complexity_tier and required_servers fields"],"output_types":["filtered task list (tasks matching selected tier)","per-tier statistics (mean score, stddev, task count)"],"categories":["planning-reasoning","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github_mcp-accenture-mcp-bench__cap_8","uri":"capability://automation.workflow.configuration.driven.benchmark.customization.with.yaml.schemas","name":"configuration-driven benchmark customization with yaml schemas","description":"Enables benchmark customization via YAML configuration files (config.yaml, tasks/benchmark_tasks.yaml) with schema validation. Supports configuring LLM providers, MCP servers, task definitions, evaluation parameters, and execution settings without code changes. Configuration is loaded at startup and validated against JSON Schema, enabling early error detection and clear error messages for misconfiguration.","intents":["Customize benchmark runs (models, tasks, servers) via configuration without code changes","Share benchmark configurations across teams for reproducible evaluation","Validate configuration correctness before expensive benchmark execution","Support multiple benchmark variants (e.g., different task subsets per model)"],"best_for":["Non-technical users customizing benchmark runs via YAML","Teams sharing benchmark configurations in version control","CI/CD pipelines with configuration-driven benchmark variants"],"limitations":["YAML configuration is static; no runtime configuration updates or dynamic parameter tuning","Schema validation is basic (type checking, required fields); no semantic validation (e.g., valid API endpoints)","No configuration inheritance or templating; each variant requires full configuration duplication","Configuration errors are caught at startup; no graceful degradation or fallback defaults"],"requires":["YAML files (config.yaml, tasks/benchmark_tasks.yaml) in project root","JSON Schema for configuration validation","Python 3.9+ with PyYAML library"],"input_types":["YAML configuration files (provider settings, task definitions, execution parameters)"],"output_types":["parsed configuration (dict with validated settings)","validation errors (list of schema violations)"],"categories":["automation-workflow","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github_mcp-accenture-mcp-bench__cap_9","uri":"capability://automation.workflow.concurrent.task.execution.with.configurable.worker.pools","name":"concurrent task execution with configurable worker pools","description":"Executes multiple benchmark tasks in parallel using a configurable worker pool (default: 4 workers), respecting per-server rate limits and MCP server concurrency caps. Workers are implemented as async tasks that pull tasks from a queue, execute agents, collect results, and push results to an output queue. Rate limiting is enforced at the server level, preventing any single worker from exceeding server quotas.","intents":["Execute 100+ benchmark tasks in hours instead of days via parallel execution","Maximize MCP server utilization without exceeding rate limits","Reduce total benchmark runtime while maintaining result quality","Enable cost-effective benchmarking by parallelizing expensive LLM calls"],"best_for":["Large-scale benchmarks (100+ tasks) where sequential execution is prohibitively slow","Teams with multiple LLM API quotas enabling parallel provider calls","Benchmark runners with access to high-concurrency MCP servers"],"limitations":["Actual parallelism is limited by MCP server rate limits — configured workers may sit idle waiting for rate limit windows","Worker pool overhead adds ~500ms per task for queue management and result collection","No dynamic worker scaling — worker count is fixed at startup","Concurrent execution introduces non-determinism — task order and timing vary across runs, affecting reproducibility"],"requires":["Python 3.9+ with asyncio support","MCP servers with documented rate limits (requests per minute)","Configuration specifying num_workers and per-server rate_limit_per_minute"],"input_types":["task queue (list of task definitions)","worker configuration (num_workers, timeout_seconds)"],"output_types":["result queue (execution results as tasks complete)","execution statistics (tasks_completed, tasks_failed, total_runtime)"],"categories":["automation-workflow","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":36,"verified":false,"data_access_risk":"high","permissions":["Python 3.9+","API credentials for at least one LLM provider (Azure OpenAI, OpenRouter, or OpenAI-compatible endpoint)","28 MCP servers installed via mcp_servers/install.sh with working subprocess execution","Network connectivity to external services (Google Maps, cryptocurrency exchanges, academic APIs)","Python 3.9+ with asyncio support","MCP servers installed and executable via subprocess (mcp_servers/install.sh)","Server configuration in mcp_servers/commands.json with rate_limit_per_minute and timeout_seconds fields","API credentials for external services (Google Maps API key, cryptocurrency exchange API keys, etc.)","Network connectivity to all 28 external services","MCP server installation via mcp_servers/install.sh"],"failure_modes":["Evaluation latency scales with task complexity — three-server tasks require sequential MCP round-trips, adding 2-5 seconds per agent step","LLM-as-judge scoring introduces variance dependent on judge model quality; results not deterministic across runs","Rate limiting per MCP server can cause task timeouts if concurrent execution exceeds server-specific thresholds","Benchmark results are snapshot-based; no continuous regression detection across model versions","Connection pooling adds ~100ms overhead on first connection per server; subsequent calls reuse connections","Rate limiting is enforced per-server but not globally — concurrent tasks may still exceed aggregate API quotas","Timeout enforcement is process-level; hung MCP server processes may block connection pool until timeout expires (default 30s)","No automatic reconnection on transient network failures; requires manual retry logic in caller","Server availability depends on external service uptime (e.g., Google Maps API, cryptocurrency exchanges)","Authentication requirements vary per server (API keys, OAuth tokens) — setup complexity increases with server count","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.35465872900261725,"quality":0.34,"ecosystem":0.39999999999999997,"match_graph":0.25,"freshness":0.6,"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":"2026-05-24T12:16:22.064Z","last_scraped_at":"2026-05-03T14:23:44.761Z","last_commit":"2025-10-07T17:22:43Z"},"community":{"stars":477,"forks":61,"weekly_downloads":null,"model_downloads":null,"model_likes":null}},"distribution":{"claim_url":"https://unfragile.ai/submit?claim=mcp-accenture-mcp-bench","compare_url":"https://unfragile.ai/compare?artifact=mcp-accenture-mcp-bench"}},"signature":"GFrF138CbPhjFHsH1m4+fsvpsoYLLVLuEbwajrdg3KCnooL/hnif+CtEs+TyIC70DIw2FpWJBlCaf4eCEGdPCA==","signedAt":"2026-06-20T09:38:55.464Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/mcp-accenture-mcp-bench","artifact":"https://unfragile.ai/mcp-accenture-mcp-bench","verify":"https://unfragile.ai/api/v1/verify?slug=mcp-accenture-mcp-bench","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"}}