agnost

Q: What can agnost do?

mcp server event instrumentation and telemetry collection, automatic tool usage analytics and adoption tracking, error and failure rate monitoring with mcp-aware categorization, latency and performance profiling for tool execution, resource access and permission tracking, prompt execution and completion tracking, agent behavior pattern detection and anomaly alerting, multi-backend analytics export and integration

MCP ServerFree

Analytics SDK for Model Context Protocol Servers

Open Source

/ 100

8 capabilities

Capabilities8 decomposed

mcp server event instrumentation and telemetry collection

Medium confidence

Agnost provides a lightweight instrumentation layer that hooks into Model Context Protocol server lifecycle events (tool calls, resource access, prompt execution) and collects structured telemetry data without requiring manual logging code. The SDK wraps MCP server handlers to automatically capture timing, error states, and request/response metadata, then buffers and batches events for efficient transmission to analytics backends.

Solves for

I need to understand which MCP tools my AI agents are actually using and how oftenI want to track performance bottlenecks in my MCP server without adding instrumentation code everywhereI need to capture errors and failures in tool execution to debug agent behaviorI want to measure latency across different MCP operations to optimize my server

Best for

MCP server developers building production AI agent infrastructure

teams deploying multi-tool MCP servers who need observability

developers debugging agent behavior through server-side telemetry

Requires

Node.js 14+ or compatible JavaScript runtime

Active MCP server implementation

Network connectivity to analytics backend (self-hosted or cloud)

Limitations

Requires MCP server to be written in a supported language/framework (likely Node.js/TypeScript based on npm distribution)

Event batching introduces latency between occurrence and analytics visibility — typically 1-5 seconds depending on batch size configuration

No built-in persistence — if analytics backend is unavailable, events may be dropped unless external buffering is configured

What makes it unique

Agnost is purpose-built for MCP protocol semantics rather than generic application monitoring — it understands tool invocation patterns, resource access hierarchies, and prompt execution flows native to MCP, allowing it to capture domain-specific metrics without requiring developers to manually define what constitutes a 'tool call' or 'resource access'

vs alternatives

Unlike generic APM tools (DataDog, New Relic) that require boilerplate instrumentation code, Agnost provides zero-config MCP-aware telemetry that automatically understands tool boundaries and resource semantics without manual span creation

automatic tool usage analytics and adoption tracking

Medium confidence

The SDK automatically tracks which tools within an MCP server are invoked, how frequently each tool is called, and patterns of tool combinations used by agents. It aggregates this data into usage metrics that show tool adoption rates, popularity trends, and which tools are unused or underutilized, enabling data-driven decisions about tool maintenance and expansion.

Solves for

I want to know which of my 50 MCP tools are actually being used by agents in productionI need to identify tools that are rarely called so I can deprecate or improve themI want to see if adding a new tool actually increased agent capability or if agents ignore itI need to understand tool usage patterns to optimize my MCP server's tool registry

Best for

MCP server maintainers managing large tool catalogs

product teams deciding which agent capabilities to invest in

developers optimizing tool design based on actual usage patterns

Requires

MCP server with Agnost SDK integrated

Analytics backend with time-series aggregation support

Minimum 24 hours of production traffic for meaningful trends

Limitations

Requires sufficient traffic volume to generate statistically meaningful usage patterns — low-traffic servers may show noisy data

Cannot distinguish between successful and failed tool invocations without additional error tracking configuration

Aggregation granularity is fixed (typically hourly or daily) — real-time per-request tool tracking requires higher-tier analytics backend

What makes it unique

Agnost's tool analytics are MCP-native, automatically parsing tool names and parameters from MCP protocol messages rather than requiring manual event tagging — it understands the MCP tool registry schema and can correlate usage with tool definitions to identify orphaned or misconfigured tools

vs alternatives

Compared to generic event analytics (Amplitude, Mixpanel), Agnost requires zero custom event instrumentation for tool tracking because it extracts tool identity directly from MCP protocol semantics, reducing implementation overhead by 80%

error and failure rate monitoring with mcp-aware categorization

Medium confidence

Agnost captures tool execution failures, resource access errors, and prompt processing failures within MCP servers, automatically categorizing them by error type (timeout, permission denied, invalid parameters, server error) and correlating them with specific tools or resources. It tracks error rates over time and identifies error patterns that indicate systemic issues in agent-tool interactions.

Solves for

I need to know which tools are failing most often and why agents can't use them reliablyI want to detect when a tool starts failing unexpectedly so I can alert the teamI need to understand if errors are due to bad agent prompts, tool bugs, or infrastructure issuesI want to track error trends to see if my tool reliability is improving or degrading

Best for

MCP server operators responsible for uptime and reliability

teams debugging agent failures caused by tool errors

developers identifying which tools need hardening or better error handling

Requires

MCP server with error handling that throws or returns error responses

Analytics backend with error aggregation and alerting capabilities

Proper error logging/exception handling in tool implementations

Limitations

Error categorization relies on exception types and messages — custom or poorly-formatted errors may be miscategorized as 'unknown'

Cannot distinguish between transient errors (network blip) and persistent failures without additional retry tracking

Does not capture partial failures (tool returns invalid data) unless explicitly surfaced as errors by the tool implementation

What makes it unique

Agnost understands MCP error semantics (tool not found, invalid parameters, resource access denied) and automatically maps them to root causes, whereas generic error tracking treats all errors as opaque strings — this enables MCP-specific alerting like 'tool X has 10% error rate due to permission denied'

vs alternatives

Unlike Sentry or Rollbar which require manual error context setup, Agnost automatically extracts error semantics from MCP protocol responses and correlates them with tool definitions, providing out-of-the-box MCP error intelligence

latency and performance profiling for tool execution

Medium confidence

The SDK measures end-to-end execution time for each tool invocation, resource access, and prompt processing operation within the MCP server, capturing timing data at multiple granularities (total time, network time, processing time). It aggregates this into performance metrics like p50, p95, p99 latencies and identifies tools with performance degradation or outliers.

Solves for

I need to identify which tools are slow and causing agent response delaysI want to track if my tool performance is degrading over time as load increasesI need to set SLAs for tool execution time and monitor complianceI want to find performance bottlenecks in my MCP server to optimize them

Best for

MCP server operators optimizing for agent latency and user experience

teams with strict SLA requirements for tool response times

developers profiling tool performance to identify optimization opportunities

Requires

MCP server with Agnost SDK integrated

Analytics backend with percentile aggregation support

Synchronized system clocks across server instances (for distributed deployments)

Limitations

Timing measurements include network overhead — cannot isolate pure tool processing time without additional instrumentation

Percentile calculations require sufficient sample size (typically 100+ samples) to be statistically meaningful

Clock skew or system time adjustments can distort latency measurements

What makes it unique

Agnost captures latency at the MCP protocol boundary, automatically measuring tool execution time without requiring developers to add timing code — it understands MCP request/response semantics and can correlate latency with tool parameters to identify parameter-dependent performance issues

vs alternatives

Compared to generic APM tools, Agnost provides MCP-native latency tracking that automatically understands tool boundaries and can correlate slow tools with specific parameters, whereas generic tools require manual span instrumentation for each tool

resource access and permission tracking

Medium confidence

Agnost monitors which resources are accessed through MCP resource endpoints, tracks access patterns and frequency, and can correlate resource access with specific tools or agents. It provides visibility into resource utilization and can detect unusual access patterns that might indicate misconfiguration or security issues.

Solves for

I want to understand which resources my agents are actually accessing through MCPI need to track if agents are accessing resources they shouldn't have permission forI want to identify unused resources that can be removed or optimizedI need to detect unusual resource access patterns that might indicate agent misbehavior

Best for

MCP server operators managing resource-heavy operations

teams with security requirements around resource access auditing

developers optimizing resource usage and identifying bottlenecks

Requires

MCP server with resource endpoints implemented

Agnost SDK integrated with resource tracking enabled

Analytics backend with resource aggregation support

Limitations

Requires MCP server to expose resource endpoints — cannot track resources accessed through tools indirectly

Does not capture resource content or sensitive data — only tracks access metadata

Cannot distinguish between authorized and unauthorized access without additional permission context

What makes it unique

Agnost integrates with MCP's resource protocol to automatically track resource access without requiring tool-level instrumentation — it understands resource URIs and hierarchies native to MCP, enabling resource-level analytics that generic tools cannot provide

vs alternatives

Unlike generic audit logging, Agnost provides MCP-aware resource analytics that automatically correlates resource access with tools and agents, enabling resource-specific insights like 'resource X is accessed 1000x/day by tool Y' without manual correlation

prompt execution and completion tracking

Medium confidence

The SDK tracks prompt processing events within MCP servers, capturing metrics about prompt execution (input tokens, output tokens, model used, execution time) and completion patterns. It enables analysis of how agents are using prompts and whether prompt modifications are improving agent effectiveness.

Solves for

I want to track token usage across all prompts in my MCP server to manage costsI need to understand if my prompt engineering changes are actually improving agent behaviorI want to identify which prompts are most expensive or slowest to executeI need to correlate prompt execution with tool success rates to optimize prompts

Best for

teams managing LLM costs and token budgets

developers iterating on prompt design with data-driven feedback

MCP server operators optimizing for cost and performance

Requires

MCP server with prompt execution instrumentation

Token counting library compatible with target models

Analytics backend with aggregation support

Limitations

Token counting accuracy depends on the model's tokenizer — estimates may differ from actual usage

Does not capture prompt content itself (for privacy/security) — only metadata about execution

Cannot measure prompt effectiveness directly — requires correlation with downstream tool success

What makes it unique

Agnost captures prompt execution at the MCP server level, automatically tracking token usage and execution time without requiring integration with specific LLM APIs — it works with any LLM backend that the MCP server uses

vs alternatives

Unlike LLM provider dashboards (OpenAI, Anthropic) that only show usage for their own models, Agnost provides unified prompt analytics across multiple LLM providers and custom models, with correlation to MCP tool usage

agent behavior pattern detection and anomaly alerting

Medium confidence

Agnost analyzes aggregated telemetry data to detect unusual patterns in agent behavior — such as sudden spikes in tool usage, error rate increases, latency degradation, or resource access anomalies. It can trigger alerts when metrics deviate from baseline behavior, enabling rapid detection of agent failures or infrastructure issues.

Solves for

I want to be alerted immediately if my agents start failing or behaving abnormallyI need to detect when a tool deployment breaks agent behavior without waiting for user complaintsI want to identify when agents are stuck in loops or making inefficient tool callsI need to detect security anomalies like agents accessing resources they shouldn't

Best for

teams running production MCP servers with SLA requirements

developers who need rapid feedback on agent behavior changes

operators managing multi-tenant or high-volume MCP deployments

Requires

Agnost SDK integrated with MCP server

Analytics backend with anomaly detection capabilities

Alert notification system (email, Slack, PagerDuty, etc.)

Limitations

Anomaly detection requires baseline data — new deployments need 24-48 hours of traffic before alerts are meaningful

Seasonal or expected traffic patterns may trigger false positives unless explicitly configured

Alerts are based on aggregate metrics — cannot detect anomalies in individual agent sessions

What makes it unique

Agnost's anomaly detection is MCP-aware, understanding tool-level and resource-level baselines rather than treating all metrics equally — it can detect 'tool X error rate increased 10x' as an anomaly while ignoring expected seasonal variations in overall traffic

vs alternatives

Unlike generic monitoring tools (Datadog, New Relic) that require manual baseline configuration, Agnost automatically learns MCP-specific baselines and can detect tool-level anomalies without requiring developers to define what constitutes 'normal' behavior

multi-backend analytics export and integration

Medium confidence

Agnost provides a pluggable backend system that allows telemetry data to be exported to multiple analytics platforms (custom HTTP endpoints, cloud analytics services, data warehouses) simultaneously. It handles batching, buffering, and retry logic for reliable event delivery across heterogeneous backends.

Solves for

I want to send MCP telemetry to my existing analytics platform (Datadog, Segment, etc.)I need to store raw telemetry events in my data warehouse for custom analysisI want to use multiple analytics tools without duplicating instrumentation codeI need reliable event delivery with automatic retries if my analytics backend is temporarily down

Best for

teams with existing analytics infrastructure they want to integrate with

organizations with data warehouse requirements

developers building custom analytics on top of MCP telemetry

Requires

Agnost SDK with backend configuration

Network connectivity to target analytics backends

Backend-specific API keys or authentication credentials

Limitations

Event delivery is at-least-once semantics — duplicate events may occur if retries happen after partial delivery

Backend-specific schema mapping must be configured manually — no automatic format translation

Buffering and batching add latency (typically 1-5 seconds) before events reach backends

What makes it unique

Agnost's backend system is designed for MCP-specific event schemas, automatically handling MCP protocol semantics (tool names, resource URIs, error types) when exporting to backends, whereas generic event exporters treat all events as opaque JSON

vs alternatives

Compared to building custom integrations for each analytics tool, Agnost provides a unified export layer that handles batching, retries, and buffering automatically, reducing integration code by 70%

Capabilities are decomposed by AI analysis. Each maps to specific user intents and improves with match feedback.

Related Artifactssharing capabilities

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mcp error and exception span recordingautomatic mcp server lifecycle span creationmcp tool call request/response span attributionopentelemetry-based mcp server request tracing

4 shared capabilities

MCP Server28

@listo-ai/mcp-observability

Lightweight telemetry SDK for MCP servers and web applications. Captures HTTP requests, MCP tool invocations, business events, and UI interactions with built-in payload sanitization.

mcp tool invocation telemetry captureperformance metrics collection and aggregation

2 shared capabilities

MCP Server36

@mcp-use/inspector

MCP Inspector - A tool for inspecting and debugging MCP servers

mcp server performance profiling and metrics collectionmcp error handling and diagnostic reporting

2 shared capabilities

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apify-mcp-server

The Apify MCP server enables your AI agents to extract data from social media, search engines, maps, e-commerce sites, or any other website using thousands of ready-made scrapers, crawlers, and automation tools available on the Apify Store.

telemetry collection and monitoring for tool usage

1 shared capability

MCP Server48

DesktopCommanderMCP

This is MCP server for Claude that gives it terminal control, file system search and diff file editing capabilities

capture and telemetry tracking for tool usage and error monitoring

1 shared capability

MCP Server20

MCPVerse

** - A portal for creating & hosting authenticated MCP servers and connecting to them securely.

mcp server monitoring and observability

1 shared capability

Best For

✓MCP server developers building production AI agent infrastructure
✓teams deploying multi-tool MCP servers who need observability
✓developers debugging agent behavior through server-side telemetry
✓MCP server maintainers managing large tool catalogs
✓product teams deciding which agent capabilities to invest in
✓developers optimizing tool design based on actual usage patterns
✓MCP server operators responsible for uptime and reliability
✓teams debugging agent failures caused by tool errors

Known Limitations

⚠Requires MCP server to be written in a supported language/framework (likely Node.js/TypeScript based on npm distribution)
⚠Event batching introduces latency between occurrence and analytics visibility — typically 1-5 seconds depending on batch size configuration
⚠No built-in persistence — if analytics backend is unavailable, events may be dropped unless external buffering is configured
⚠Limited to MCP protocol events; cannot capture client-side agent decision-making or reasoning steps
⚠Requires sufficient traffic volume to generate statistically meaningful usage patterns — low-traffic servers may show noisy data
⚠Cannot distinguish between successful and failed tool invocations without additional error tracking configuration

Requirements

Node.js 14+ or compatible JavaScript runtimeActive MCP server implementationNetwork connectivity to analytics backend (self-hosted or cloud)npm or yarn package managerMCP server with Agnost SDK integratedAnalytics backend with time-series aggregation supportMinimum 24 hours of production traffic for meaningful trendsMCP server with error handling that throws or returns error responses

Input / Output

Accepts: MCP server handler functions, Tool call requests with parameters, Resource access patterns, Error objects and stack traces, tool invocation events with tool name and parameters, timestamp and execution context, exception objects and error messages, HTTP status codes or MCP error responses, tool execution context (which tool, which parameters), tool invocation start/end timestamps, execution context and tool identity, resource access requests with resource URI, access timestamp and context, resource metadata (size, type), prompt execution events with token counts, model identifier and execution time, completion status, aggregated telemetry metrics (error rates, latencies, usage counts), historical baseline data, alert threshold configuration, telemetry events (structured JSON), backend configuration (endpoint, auth, format)

Produces: structured telemetry events (JSON), aggregated analytics metrics, time-series performance data, tool usage counts (absolute and relative), adoption rate percentages, usage trend data (time-series), tool popularity rankings, error rate percentages per tool, error type distribution, error trend data, error correlation with tool or resource, latency percentiles (p50, p95, p99), average/median execution time per tool, latency trend data, outlier detection and alerts, resource access counts and frequency, resource popularity rankings, access pattern trends, anomaly detection alerts, token usage metrics (total, average, distribution), cost estimates based on model pricing, execution time per prompt, completion rate and quality metrics, deviation magnitude and direction, affected tools or resources, recommended actions, events exported to configured backends, delivery status and retry logs, backend-specific formatted data

UnfragileRank

Adoption43%(30% weight)

Quality17%(25% weight)

Ecosystem55%(25% weight)

Match Graph10%(15% weight)

Freshness75%(5% weight)

UnfragileRank is computed from adoption signals, documentation quality, ecosystem connectivity, match graph feedback, and freshness. No artifact can pay for a higher rank.

Type: MCP Server

8 capabilities

Visit agnost→

Repository Details

Package Details

npm

Registry

0.1.10

Version

7,065

Weekly Downloads

About

Analytics SDK for Model Context Protocol Servers

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Capabilities8 decomposed

mcp server event instrumentation and telemetry collection

Medium confidence

Solves for

Best for

MCP server developers building production AI agent infrastructure

teams deploying multi-tool MCP servers who need observability

developers debugging agent behavior through server-side telemetry

Requires

Node.js 14+ or compatible JavaScript runtime

Active MCP server implementation

Network connectivity to analytics backend (self-hosted or cloud)

Limitations

Requires MCP server to be written in a supported language/framework (likely Node.js/TypeScript based on npm distribution)

Event batching introduces latency between occurrence and analytics visibility — typically 1-5 seconds depending on batch size configuration

No built-in persistence — if analytics backend is unavailable, events may be dropped unless external buffering is configured

What makes it unique

vs alternatives

automatic tool usage analytics and adoption tracking

Medium confidence

Solves for

Best for

MCP server maintainers managing large tool catalogs

product teams deciding which agent capabilities to invest in

developers optimizing tool design based on actual usage patterns

Requires

MCP server with Agnost SDK integrated

Analytics backend with time-series aggregation support

Minimum 24 hours of production traffic for meaningful trends

Limitations

Requires sufficient traffic volume to generate statistically meaningful usage patterns — low-traffic servers may show noisy data

Cannot distinguish between successful and failed tool invocations without additional error tracking configuration

Aggregation granularity is fixed (typically hourly or daily) — real-time per-request tool tracking requires higher-tier analytics backend

What makes it unique

vs alternatives

error and failure rate monitoring with mcp-aware categorization

Medium confidence

Solves for

Best for

MCP server operators responsible for uptime and reliability

teams debugging agent failures caused by tool errors

developers identifying which tools need hardening or better error handling

Requires

MCP server with error handling that throws or returns error responses

Analytics backend with error aggregation and alerting capabilities

Proper error logging/exception handling in tool implementations

Limitations

Error categorization relies on exception types and messages — custom or poorly-formatted errors may be miscategorized as 'unknown'

Cannot distinguish between transient errors (network blip) and persistent failures without additional retry tracking

Does not capture partial failures (tool returns invalid data) unless explicitly surfaced as errors by the tool implementation

What makes it unique

vs alternatives

latency and performance profiling for tool execution

Medium confidence

Solves for

Best for

MCP server operators optimizing for agent latency and user experience

teams with strict SLA requirements for tool response times

developers profiling tool performance to identify optimization opportunities

Requires

MCP server with Agnost SDK integrated

Analytics backend with percentile aggregation support

Synchronized system clocks across server instances (for distributed deployments)

Limitations

Timing measurements include network overhead — cannot isolate pure tool processing time without additional instrumentation

Percentile calculations require sufficient sample size (typically 100+ samples) to be statistically meaningful

Clock skew or system time adjustments can distort latency measurements

What makes it unique

vs alternatives

resource access and permission tracking

Medium confidence

Solves for

Best for

MCP server operators managing resource-heavy operations

teams with security requirements around resource access auditing

developers optimizing resource usage and identifying bottlenecks

Requires

MCP server with resource endpoints implemented

Agnost SDK integrated with resource tracking enabled

Analytics backend with resource aggregation support

Limitations

Requires MCP server to expose resource endpoints — cannot track resources accessed through tools indirectly

Does not capture resource content or sensitive data — only tracks access metadata

Cannot distinguish between authorized and unauthorized access without additional permission context

What makes it unique

vs alternatives

prompt execution and completion tracking

Medium confidence

Solves for

Best for

teams managing LLM costs and token budgets

developers iterating on prompt design with data-driven feedback

MCP server operators optimizing for cost and performance

Requires

MCP server with prompt execution instrumentation

Token counting library compatible with target models

Analytics backend with aggregation support

Limitations

Token counting accuracy depends on the model's tokenizer — estimates may differ from actual usage

Does not capture prompt content itself (for privacy/security) — only metadata about execution

Cannot measure prompt effectiveness directly — requires correlation with downstream tool success

What makes it unique

vs alternatives

agent behavior pattern detection and anomaly alerting

Medium confidence

Solves for

Best for

teams running production MCP servers with SLA requirements

developers who need rapid feedback on agent behavior changes

operators managing multi-tenant or high-volume MCP deployments

Requires

Agnost SDK integrated with MCP server

Analytics backend with anomaly detection capabilities

Alert notification system (email, Slack, PagerDuty, etc.)

Limitations

Anomaly detection requires baseline data — new deployments need 24-48 hours of traffic before alerts are meaningful

Seasonal or expected traffic patterns may trigger false positives unless explicitly configured

Alerts are based on aggregate metrics — cannot detect anomalies in individual agent sessions

What makes it unique

vs alternatives

multi-backend analytics export and integration

Medium confidence

Solves for

Best for

teams with existing analytics infrastructure they want to integrate with

organizations with data warehouse requirements

developers building custom analytics on top of MCP telemetry

Requires

Agnost SDK with backend configuration

Network connectivity to target analytics backends

Backend-specific API keys or authentication credentials

Limitations

Event delivery is at-least-once semantics — duplicate events may occur if retries happen after partial delivery

Backend-specific schema mapping must be configured manually — no automatic format translation

Buffering and batching add latency (typically 1-5 seconds) before events reach backends

What makes it unique

vs alternatives

Compared to building custom integrations for each analytics tool, Agnost provides a unified export layer that handles batching, retries, and buffering automatically, reducing integration code by 70%

Capabilities are decomposed by AI analysis. Each maps to specific user intents and improves with match feedback.

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agnost

Capabilities8 decomposed

mcp server event instrumentation and telemetry collection

automatic tool usage analytics and adoption tracking

error and failure rate monitoring with mcp-aware categorization

latency and performance profiling for tool execution

resource access and permission tracking

prompt execution and completion tracking

agent behavior pattern detection and anomaly alerting

multi-backend analytics export and integration

Related Artifactssharing capabilities

@traceloop/instrumentation-mcp

@listo-ai/mcp-observability

@mcp-use/inspector

apify-mcp-server

DesktopCommanderMCP

MCPVerse

Best For

Known Limitations

Requirements

Input / Output

UnfragileRank

Repository Details

Package Details

About

Categories

Alternatives to agnost

Are you the builder of agnost?

Get the weekly brief

Data Sources

agnost

Capabilities8 decomposed

mcp server event instrumentation and telemetry collection

automatic tool usage analytics and adoption tracking

error and failure rate monitoring with mcp-aware categorization

latency and performance profiling for tool execution

resource access and permission tracking

prompt execution and completion tracking

agent behavior pattern detection and anomaly alerting

multi-backend analytics export and integration

Related Artifactssharing capabilities

@traceloop/instrumentation-mcp

@listo-ai/mcp-observability

@mcp-use/inspector

apify-mcp-server

DesktopCommanderMCP

MCPVerse

Best For

Known Limitations

Requirements

Input / Output

UnfragileRank

Repository Details

Package Details

About

Categories

Alternatives to agnost

Are you the builder of agnost?

Get the weekly brief

Data Sources