Agent Observability

Trace AI agent execution: every tool call, every error, every dollar. Open source, local-first.

Quick Install

Automated & IDE Setup

Copy the AI prompt to install this server into Claude Code, Cursor, or another agent — or use 1-click editor setup below.

Add to Cursor Add to VS Code

Not yet automatically verified

We haven't yet run this listing's install command through our automated sandbox check. This isn't a red flag — we're steadily working through the catalog.

Manual Client & Custom JSON ConfigExpand JSON ▾

Client Config & Setup

Choose your client or environment

Target File:~/Library/Application Support/Claude/claude_desktop_config.json

claude_desktop_config.json

{
  "mcpServers": {
    "agent-observability": {
      "command": "npx",
      "args": [
        "-y",
        "agent-obs@latest"
      ]
    }
  }
}

💡 Paste the JSON block into your client's configuration file under mcpServers, then restart the application.

Capabilities & Tool Schemas (6)Self-reported

Inspect callable tools, capabilities, and parameters exposed to AI agents by Agent Observability.

obs_record_tool_call

{ "id": "call-uuid", "recorded": true }

obs_record_token_usage

{ "totalTokens": 1850, "estimatedCost": "$0.023" }

obs_record_decision

{ "id": "decision-uuid", "recorded": true }

obs_record_grade

{ "grade": "B", "recorded": true }

obs_get_session_report

Full session JSON with all calls, tokens, decisions, grade

obs_list_sessions

Array of `{ id, description, grade, createdAt, tokenTotal, estimatedCost }

Documentation Overview

agent-observability

Open source agent observability — see what your agents did, why they failed, and what it cost. Runs locally. No cloud required.

Quick Start

One command. No manual config. No copy-paste.

Terminal

npx agent-obs@latest setup

This auto-detects your AI agent (opencode, Claude Code, Cursor) and configures everything:

Adds agent-obs to your MCP config
Adds the self-reporting instruction
Tells you what to do next

Then restart your agent. Every action will now self-report.

bash

agent-obs dashboard    # http://localhost:9400 — see your sessions appear

What you'll see

After running a task in your agent, the dashboard shows:

Every tool call with duration and status
A-F session grade (how efficient was your agent?)
Cost per session in tokens and dollars
Full audit trail of what was read, edited, and executed

Manual setup (if `setup` can't detect your agent)

opencode: Add to opencode.json:

config.json

{
  "mcp": {
    "agent-obs": {
      "command": "npx",
      "args": ["-y", "agent-obs@latest", "server"],
      "enabled": true
    }
  }
}

Claude Code: Create .mcp.json:

config.json

{
  "mcpServers": {
    "agent-obs": {
      "command": "npx",
      "args": ["-y", "agent-obs@latest", "server"]
    }
  }
}

Cursor: Add to Cursor Settings > MCP:

config.json

{
  "mcpServers": {
    "agent-obs": {
      "command": "npx",
      "args": ["-y", "agent-obs@latest", "server"]
    }
  }
}

See Agent-Specific Setup below for detailed per-platform instructions including self-reporting directives.

Note: All data is written to a local SQLite database in ~/.agent-observability/. No data leaves your machine.

Proxy mode (fallback)

For agents that can't self-report, wrap any MCP server and every tool invocation gets traced automatically:

bash

agent-obs proxy --desc "fix login bug" -- npx @modelcontextprotocol/server-filesystem /tmp

Proxy mode captures only MCP tool calls (~30% of typical agent actions). Prefer the setup / MCP server approach above.

Agent-Specific Setup

Claude Code

MCP Config — Create .mcp.json in your project root:

config.json

{
  "mcpServers": {
    "agent-obs": {
      "command": "npx",
      "args": ["-y", "agent-obs@latest", "server"]
    }
  }
}

Self-reporting instruction — Create .claude/instructions.md (or reference the existing SKILL.md under .claude/skills/agent-obs/SKILL.md):

markdown

After every tool call, report to agent-obs's log_tool_call.
Start each task with start_session. End with end_session.

Verify — Run agent-obs dashboard, open http://localhost:9400, and look for your session after the agent completes a task.

Cursor

MCP Config — Add to Cursor's MCP settings (~/.cursor/mcp.json, or Settings → MCP → Add new global MCP server):

config.json

{
  "mcpServers": {
    "agent-obs": {
      "command": "npx",
      "args": ["-y", "agent-obs@latest", "server"]
    }
  }
}

Self-reporting instruction — Create .cursorrules (or .cursor/rules/agent-obs.md):

Code

After every tool call, you MUST call the agent-obs MCP server's log_tool_call tool.
Include toolName, status (success/error), outputSummary, and durationMs.
Start each session with start_session and end with end_session.

Verify — Run agent-obs dashboard, open http://localhost:9400, and look for your session after the agent completes a task.

Architecture

server.ts

                   ┌──────────────────────────┐
                   │   agent-obs server       │
                   │    (self-reporting)      │
                   │                          │
                   │ Agent calls obs_record_* │
                   │ tools directly via MCP   │
                   │ • tool calls             │
                   │ • token usage            │
                   │ • decisions              │
                   │ • grades                 │
                   └──────────┬───────────────┘
                              │
                              ▼
                   ┌──────────────────────────┐
                   │     agent-obs proxy      │
                   │ (fallback — MCP-only)    │
                   │                          │
MCP Client ───────▶│ intercepts tool calls ──▶│ MCP Server
  (Claude/Cursor)   │ logs to SQLite           │  (filesystem,
                   └──────────┬───────────────┘   github, etc.)
                              │
                              ▼
                   ┌──────────────────────────┐
                   │   ~/.agent-observability │
                   │       SQLite DB          │
                   │                          │
                   │  • sessions              │
                   │  • tool_calls            │
                   │  • token_usage           │
                   │  • decisions             │
                   │  • grades                │
                   └──────────┬───────────────┘
                              │
                              ▼
                   ┌──────────────────────────┐
                   │   agent-obs dashboard    │
                   │     (port 9400)          │
                   │                          │
                   │  GET  /api/sessions      │
                   │  GET  /api/sessions/:id  │
                   │  GET  /api/search?q=     │
                   │  GET  /api/stats         │
                   │  POST /api/export        │
                   └──────────────────────────┘

API Reference

REST Endpoints (Dashboard)

Method	Path	Description
`GET`	`/api/sessions`	List all sessions. Query params: `?limit=20&offset=0&grade=B`
`GET`	`/api/sessions/:id`	Get full session detail with all tool calls, tokens, decisions, and grades
`GET`	`/api/sessions/:id/tool-calls`	List tool calls for a session. Query params: `?status=error&server=filesystem`
`GET`	`/api/sessions/:id/tokens`	Get token usage history for a session
`GET`	`/api/sessions/:id/decisions`	Get decision points for a session
`GET`	`/api/search`	Full-text search across tool call inputs/outputs. Query param: `?q=read_file`
`GET`	`/api/stats`	Aggregate statistics: total sessions, avg grade, total tokens, total cost
`POST`	`/api/export`	Export session data as JSON. Body: `{ "sessionIds": ["abc123"], "format": "json" }`
`GET`	`/api/health`	Health check. Returns `{ "status": "ok", "dbSize": "2.4MB", "sessionCount": 47 }`

MCP Tools (Server Mode)

Tool	Parameters	Returns
`obs_record_tool_call`	`sessionId`, `toolName`, `serverName`, `duration` (ms), `status` (success/error), `input`, `output`	`{ "id": "call-uuid", "recorded": true }`
`obs_record_token_usage`	`sessionId`, `inputTokens`, `outputTokens`, `model`	`{ "totalTokens": 1850, "estimatedCost": "$0.023" }`
`obs_record_decision`	`sessionId`, `context`, `options`, `chosen`, `reasoning`	`{ "id": "decision-uuid", "recorded": true }`
`obs_record_grade`	`sessionId`, `grade` (A/B/C/D/F), `reasoning`	`{ "grade": "B", "recorded": true }`
`obs_get_session_report`	`sessionId`	Full session JSON with all calls, tokens, decisions, grade
`obs_list_sessions`	`limit`, `offset`	Array of `{ id, description, grade, createdAt, tokenTotal, estimatedCost }`

What Gets Tracked

Every tool call

Tool name — e.g. read_file, execute_command, search_code
Server — which MCP server handled it
Duration — wall-clock milliseconds
Status — success or error
Input — full arguments passed to the tool
Output — full result returned (truncated at 64KB for storage)

Token consumption

Input tokens — prompt and context
Output tokens — generated response
Total tokens — sum per session
Per-call breakdown — token delta for each LLM round-trip

Cost estimation

Costs are estimated based on published API pricing:

Model	Input (per 1M tokens)	Output (per 1M tokens)
Claude 3.5 Sonnet	$3.00	$15.00
Claude 3 Opus	$15.00	$75.00
GPT-4o	$2.50	$10.00
GPT-4 Turbo	$10.00	$30.00

Costs are tracked per session and displayed in the dashboard. You can add custom model pricing via ~/.agent-observability/models.json.

Session grades

Every session receives a letter grade based on efficiency and correctness:

Grade	Label	Criteria
A	Clean	Zero errors, minimal token waste, no unnecessary tool calls
B	Minor issues	Some inefficiencies, but no failures
C	Inefficient	Excessive token usage, redundant tool calls, recoverable errors
D	Risky	Significant problems — failed tool calls, high cost, wrong tools chosen
F	Failed	Errors prevented task completion, or agent abandoned the session

Decision points

When the agent has multiple possible actions, it can record why it chose one over another. Example:

config.json

{
  "context": "need to read a file — it could be at src/config.ts or lib/config.ts",
  "options": ["read_file src/config.ts", "glob **/config.ts", "grep 'CONFIG' *.ts"],
  "chosen": "glob **/config.ts",
  "reasoning": "File might not exist at the expected path; glob guarantees finding it regardless of location"
}

Decision points create an audit trail of agent reasoning, making it possible to understand not just what happened, but why.

Read the full README →View source on GitHub →

Related MCP Servers

View all in Monitoring View all alternatives

Adoption & maintenance

Factual signals from GitHub, npm, and our automated checks — not a rating.

GitHub stars

Stargazers on the source repository.

Last commit

1mo ago

Most recent push to the default branch.

Tools exposed

Callable tools this server registers over MCP.

Directory activity

1 views

Config copies, upvotes, and views on AllMCPs.

Reviews

No reviews yet — be the first to share how this listing worked for you.

Frequently Asked Questions about Agent Observability

Add the following block to your claude_desktop_config.json under mcpServers: "mcpServers": { "agent-observability": { "command": "npx", "args": ["-y","agent-obs@latest"] } }

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Markdown (GitHub README)

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Technical Specs & Signals

CategoryMonitoring

3/4 checks healthy over the last 46d

51Quality signal: Good · 51/100How this signal is calculated ▾

Server availabilityNot measured

Not scored for repo-hosted servers — we can't reach the running server, only its GitHub page. Hosted MCP endpoints are health-checked live.

Verified ownership10/20

Documentation & tools25/30

Adoption & activity3/15

Community engagement0/10

A guidance signal from public completeness & health data — not a user rating. New listings start lower and rise as they add docs, get verified, and grow adoption. Signals we can't observe for a listing are skipped, not counted against it.

Supply-chain signal

No high-severity advisories surfaced by our automated scan.

Critical 0High 0Medium 0Low 0

Scanned 6d ago via OSV.dev · agent-obs@latest (npm)

★ Featured

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MCP & Agent Skills for Automated Documentation, and codebase conventions + context

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Explore more

More in 📊 Monitoring →Best MCP servers for Monitoring & Observability →Alternatives to Agent Observability →Install in Claude Desktop Install in Cursor Install in VS Code Setup guides for all 13 MCP clients

Agent Observability

Trace AI agent execution: every tool call, every error, every dollar. Open source, local-first.

Quick Install

Automated & IDE Setup

Copy the AI prompt to install this server into Claude Code, Cursor, or another agent — or use 1-click editor setup below.

Add to Cursor Add to VS Code

Not yet automatically verified

We haven't yet run this listing's install command through our automated sandbox check. This isn't a red flag — we're steadily working through the catalog.

Manual Client & Custom JSON ConfigExpand JSON ▾

Client Config & Setup

Choose your client or environment

Target File:~/Library/Application Support/Claude/claude_desktop_config.json

claude_desktop_config.json

{
  "mcpServers": {
    "agent-observability": {
      "command": "npx",
      "args": [
        "-y",
        "agent-obs@latest"
      ]
    }
  }
}

💡 Paste the JSON block into your client's configuration file under mcpServers, then restart the application.

Capabilities & Tool Schemas (6)Self-reported

Inspect callable tools, capabilities, and parameters exposed to AI agents by Agent Observability.

obs_record_tool_call

{ "id": "call-uuid", "recorded": true }

obs_record_token_usage

{ "totalTokens": 1850, "estimatedCost": "$0.023" }

obs_record_decision

{ "id": "decision-uuid", "recorded": true }

obs_record_grade

{ "grade": "B", "recorded": true }

obs_get_session_report

Full session JSON with all calls, tokens, decisions, grade

obs_list_sessions

Array of `{ id, description, grade, createdAt, tokenTotal, estimatedCost }

Documentation Overview

agent-observability

Open source agent observability — see what your agents did, why they failed, and what it cost. Runs locally. No cloud required.

Quick Start

One command. No manual config. No copy-paste.

Terminal

npx agent-obs@latest setup

This auto-detects your AI agent (opencode, Claude Code, Cursor) and configures everything:

Adds agent-obs to your MCP config
Adds the self-reporting instruction
Tells you what to do next

Then restart your agent. Every action will now self-report.

bash

agent-obs dashboard    # http://localhost:9400 — see your sessions appear

What you'll see

After running a task in your agent, the dashboard shows:

Every tool call with duration and status
A-F session grade (how efficient was your agent?)
Cost per session in tokens and dollars
Full audit trail of what was read, edited, and executed

Manual setup (if `setup` can't detect your agent)

opencode: Add to opencode.json:

config.json

{
  "mcp": {
    "agent-obs": {
      "command": "npx",
      "args": ["-y", "agent-obs@latest", "server"],
      "enabled": true
    }
  }
}

Claude Code: Create .mcp.json:

config.json

{
  "mcpServers": {
    "agent-obs": {
      "command": "npx",
      "args": ["-y", "agent-obs@latest", "server"]
    }
  }
}

Cursor: Add to Cursor Settings > MCP:

config.json

{
  "mcpServers": {
    "agent-obs": {
      "command": "npx",
      "args": ["-y", "agent-obs@latest", "server"]
    }
  }
}

See Agent-Specific Setup below for detailed per-platform instructions including self-reporting directives.

Note: All data is written to a local SQLite database in ~/.agent-observability/. No data leaves your machine.

Proxy mode (fallback)

For agents that can't self-report, wrap any MCP server and every tool invocation gets traced automatically:

bash

agent-obs proxy --desc "fix login bug" -- npx @modelcontextprotocol/server-filesystem /tmp

Proxy mode captures only MCP tool calls (~30% of typical agent actions). Prefer the setup / MCP server approach above.

Agent-Specific Setup

Claude Code

MCP Config — Create .mcp.json in your project root:

config.json

{
  "mcpServers": {
    "agent-obs": {
      "command": "npx",
      "args": ["-y", "agent-obs@latest", "server"]
    }
  }
}

Self-reporting instruction — Create .claude/instructions.md (or reference the existing SKILL.md under .claude/skills/agent-obs/SKILL.md):

markdown

After every tool call, report to agent-obs's log_tool_call.
Start each task with start_session. End with end_session.

Verify — Run agent-obs dashboard, open http://localhost:9400, and look for your session after the agent completes a task.

Cursor

MCP Config — Add to Cursor's MCP settings (~/.cursor/mcp.json, or Settings → MCP → Add new global MCP server):

config.json

{
  "mcpServers": {
    "agent-obs": {
      "command": "npx",
      "args": ["-y", "agent-obs@latest", "server"]
    }
  }
}

Self-reporting instruction — Create .cursorrules (or .cursor/rules/agent-obs.md):

Code

After every tool call, you MUST call the agent-obs MCP server's log_tool_call tool.
Include toolName, status (success/error), outputSummary, and durationMs.
Start each session with start_session and end with end_session.

Verify — Run agent-obs dashboard, open http://localhost:9400, and look for your session after the agent completes a task.

Architecture

server.ts

                   ┌──────────────────────────┐
                   │   agent-obs server       │
                   │    (self-reporting)      │
                   │                          │
                   │ Agent calls obs_record_* │
                   │ tools directly via MCP   │
                   │ • tool calls             │
                   │ • token usage            │
                   │ • decisions              │
                   │ • grades                 │
                   └──────────┬───────────────┘
                              │
                              ▼
                   ┌──────────────────────────┐
                   │     agent-obs proxy      │
                   │ (fallback — MCP-only)    │
                   │                          │
MCP Client ───────▶│ intercepts tool calls ──▶│ MCP Server
  (Claude/Cursor)   │ logs to SQLite           │  (filesystem,
                   └──────────┬───────────────┘   github, etc.)
                              │
                              ▼
                   ┌──────────────────────────┐
                   │   ~/.agent-observability │
                   │       SQLite DB          │
                   │                          │
                   │  • sessions              │
                   │  • tool_calls            │
                   │  • token_usage           │
                   │  • decisions             │
                   │  • grades                │
                   └──────────┬───────────────┘
                              │
                              ▼
                   ┌──────────────────────────┐
                   │   agent-obs dashboard    │
                   │     (port 9400)          │
                   │                          │
                   │  GET  /api/sessions      │
                   │  GET  /api/sessions/:id  │
                   │  GET  /api/search?q=     │
                   │  GET  /api/stats         │
                   │  POST /api/export        │
                   └──────────────────────────┘

API Reference

REST Endpoints (Dashboard)

Method	Path	Description
`GET`	`/api/sessions`	List all sessions. Query params: `?limit=20&offset=0&grade=B`
`GET`	`/api/sessions/:id`	Get full session detail with all tool calls, tokens, decisions, and grades
`GET`	`/api/sessions/:id/tool-calls`	List tool calls for a session. Query params: `?status=error&server=filesystem`
`GET`	`/api/sessions/:id/tokens`	Get token usage history for a session
`GET`	`/api/sessions/:id/decisions`	Get decision points for a session
`GET`	`/api/search`	Full-text search across tool call inputs/outputs. Query param: `?q=read_file`
`GET`	`/api/stats`	Aggregate statistics: total sessions, avg grade, total tokens, total cost
`POST`	`/api/export`	Export session data as JSON. Body: `{ "sessionIds": ["abc123"], "format": "json" }`
`GET`	`/api/health`	Health check. Returns `{ "status": "ok", "dbSize": "2.4MB", "sessionCount": 47 }`

MCP Tools (Server Mode)

Tool	Parameters	Returns
`obs_record_tool_call`	`sessionId`, `toolName`, `serverName`, `duration` (ms), `status` (success/error), `input`, `output`	`{ "id": "call-uuid", "recorded": true }`
`obs_record_token_usage`	`sessionId`, `inputTokens`, `outputTokens`, `model`	`{ "totalTokens": 1850, "estimatedCost": "$0.023" }`
`obs_record_decision`	`sessionId`, `context`, `options`, `chosen`, `reasoning`	`{ "id": "decision-uuid", "recorded": true }`
`obs_record_grade`	`sessionId`, `grade` (A/B/C/D/F), `reasoning`	`{ "grade": "B", "recorded": true }`
`obs_get_session_report`	`sessionId`	Full session JSON with all calls, tokens, decisions, grade
`obs_list_sessions`	`limit`, `offset`	Array of `{ id, description, grade, createdAt, tokenTotal, estimatedCost }`

What Gets Tracked

Every tool call

Tool name — e.g. read_file, execute_command, search_code
Server — which MCP server handled it
Duration — wall-clock milliseconds
Status — success or error
Input — full arguments passed to the tool
Output — full result returned (truncated at 64KB for storage)

Token consumption

Input tokens — prompt and context
Output tokens — generated response
Total tokens — sum per session
Per-call breakdown — token delta for each LLM round-trip

Cost estimation

Costs are estimated based on published API pricing:

Model	Input (per 1M tokens)	Output (per 1M tokens)
Claude 3.5 Sonnet	$3.00	$15.00
Claude 3 Opus	$15.00	$75.00
GPT-4o	$2.50	$10.00
GPT-4 Turbo	$10.00	$30.00

Costs are tracked per session and displayed in the dashboard. You can add custom model pricing via ~/.agent-observability/models.json.

Session grades

Every session receives a letter grade based on efficiency and correctness:

Grade	Label	Criteria
A	Clean	Zero errors, minimal token waste, no unnecessary tool calls
B	Minor issues	Some inefficiencies, but no failures
C	Inefficient	Excessive token usage, redundant tool calls, recoverable errors
D	Risky	Significant problems — failed tool calls, high cost, wrong tools chosen
F	Failed	Errors prevented task completion, or agent abandoned the session

Decision points

When the agent has multiple possible actions, it can record why it chose one over another. Example:

config.json

{
  "context": "need to read a file — it could be at src/config.ts or lib/config.ts",
  "options": ["read_file src/config.ts", "glob **/config.ts", "grep 'CONFIG' *.ts"],
  "chosen": "glob **/config.ts",
  "reasoning": "File might not exist at the expected path; glob guarantees finding it regardless of location"
}

Decision points create an audit trail of agent reasoning, making it possible to understand not just what happened, but why.

Read the full README →View source on GitHub →

Related MCP Servers

View all in Monitoring View all alternatives

Adoption & maintenance

Factual signals from GitHub, npm, and our automated checks — not a rating.

GitHub stars

Stargazers on the source repository.

Last commit

1mo ago

Most recent push to the default branch.

Tools exposed

Callable tools this server registers over MCP.

Directory activity

1 views

Config copies, upvotes, and views on AllMCPs.

Reviews

No reviews yet — be the first to share how this listing worked for you.

Frequently Asked Questions about Agent Observability

Add the following block to your claude_desktop_config.json under mcpServers: "mcpServers": { "agent-observability": { "command": "npx", "args": ["-y","agent-obs@latest"] } }

AllMCPs Directory Badge

Full Badge Customizer

Showcase your server listing on GitHub or your project documentation. Embed this dynamic SVG badge to highlight official listing status and live engagement.

Badge Style:

Live Dynamic SVG Preview

Markdown (GitHub README)

[![AllMCPs](https://allmcps.com/api/badge/agent-observability?style=directory)](https://allmcps.com/mcp/agent-observability)

HTML Embed

<a href="https://allmcps.com/mcp/agent-observability"><img src="https://allmcps.com/api/badge/agent-observability?style=directory" alt="Agent Observability on AllMCPs" /></a>

Technical Specs & Signals

CategoryMonitoring

3/4 checks healthy over the last 46d

51Quality signal: Good · 51/100How this signal is calculated ▾

Server availabilityNot measured

Not scored for repo-hosted servers — we can't reach the running server, only its GitHub page. Hosted MCP endpoints are health-checked live.

Verified ownership10/20

Documentation & tools25/30

Adoption & activity3/15

Community engagement0/10

Supply-chain signal

No high-severity advisories surfaced by our automated scan.

Critical 0High 0Medium 0Low 0

Scanned 6d ago via OSV.dev · agent-obs@latest (npm)

★ Featured

Moxie Docs MCP

MCP & Agent Skills for Automated Documentation, and codebase conventions + context

Explore Server →

Own this project?

Free dofollow backlink: add your website and place the AllMCPs badge on it — no claim needed. We detect it automatically and keep it verified as long as the badge stays live.

Claim & get free dofollow

Share & Embed

Add our SVG badge (dark/light directory styles) or embeddable widget to your site.

Agent Observability

Quick Install

Client Config & Setup

Documentation Overview

agent-observability

Quick Start

What you'll see

Manual setup (if setup can't detect your agent)

Proxy mode (fallback)

Agent-Specific Setup

Claude Code

Cursor

Architecture

API Reference

REST Endpoints (Dashboard)

MCP Tools (Server Mode)

What Gets Tracked

Every tool call

Token consumption

Cost estimation

Session grades

Decision points

Related MCP Servers

Adoption & maintenance

Reviews

Frequently Asked Questions about Agent Observability

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Technical Specs & Signals

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Moxie Docs MCP

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Agent Observability

Quick Install

Client Config & Setup

Documentation Overview

agent-observability

Quick Start

What you'll see

Manual setup (if setup can't detect your agent)

Proxy mode (fallback)

Agent-Specific Setup

Claude Code

Cursor

Architecture

API Reference

REST Endpoints (Dashboard)

MCP Tools (Server Mode)

What Gets Tracked

Every tool call

Token consumption

Cost estimation

Session grades

Decision points

Related MCP Servers

Adoption & maintenance

Reviews

Frequently Asked Questions about Agent Observability

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Technical Specs & Signals

Supply-chain signal

Moxie Docs MCP

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Manual setup (if `setup` can't detect your agent)

Manual setup (if `setup` can't detect your agent)