analyze_manifest
Score CLAUDE.md / AGENTS.md manifest content with the same rules as the Clarx manifest studio. Returns an overall quality estimate (0-100), per-pillar scores, findings with line numbers, and a section checklist. This is a manifest quality estimate — do not compare it to Clarx repo AI-readiness sc...
This record as markdown: /tools/ai-clarx-mcp/analyze-manifest.md
What analyze_manifest does on Mcp
AI agents call analyze_manifest to retrieve information from Mcp without modifying anything. It is typically the context-gathering step in research, monitoring, and reporting workflows, before the agent takes action elsewhere.
| Parameter | Type | Required | Description |
|---|---|---|---|
content | string | — | Full markdown body of the manifest. Pass this or file_path, not both. |
file_path | string | — | Path to the manifest file (resolved against the server working directory, typically the workspace root). Preferred — avoids inlining the file into the conversat |
include_outline | boolean | — | Include the heading outline (section tree with line numbers). |
Parameters from the server's own tool schema.
Why analyze_manifest is rated Low
This tool retrieves and analyzes manifest data to provide a quality assessment. It does not create, modify, delete, or execute any operations—it only reads manifest files and returns analytical results. The retrieval and scoring of manifest content with no side effects places it firmly in the Read category.
From the tool's definition The tool 'analyze_manifest' scores and analyzes manifest content, returning quality estimates, per-pillar scores, findings, and checklists.
Risk signalsAccepts file system path (file_path) · Accepts raw HTML/template content (content)
Attacks that exploit this kind of access
The rule that runs analyze_manifest safely
PolicyLayer is an MCP gateway: it sits between your AI agents and Mcp, and checks every tool call against a rule you set before the call runs. Nothing changes on the server itself. For analyze_manifest, this is the rule to start with:
analyze_manifest is read-only, so it stays allowed. Everything else on the server is denied unless you say otherwise.
The button opens the PolicyLayer dashboard: create your workspace, connect Mcp, apply this rule, and every analyze_manifest call is checked against it from then on.
Questions about analyze_manifest
Score CLAUDE.md / AGENTS.md manifest content with the same rules as the Clarx manifest studio. Returns an overall quality estimate (0-100), per-pillar scores, findings with line numbers, and a section checklist. This is a manifest quality estimate — do not compare it to Clarx repo AI-readiness scores, which use a different rule system. It is categorised as a Read tool in the Mcp MCP Server, which means it retrieves data without modifying state.
analyze_manifest accepts 3 parameters: content, file_path, include_outline. The full parameter table on this page comes from the server's own tool schema.
Register the MCP server in PolicyLayer and add a rule for analyze_manifest: allow, deny, rate-limit, or require approval. Point your MCP client at the PolicyLayer proxy URL and the rule is enforced on every call, before it reaches Mcp. Nothing to install.
analyze_manifest is a Read tool with low risk. Read-only tools are generally safe to allow by default.
Yes. Add a rate_limit block to the analyze_manifest rule in your PolicyLayer policy. For example, setting max: 10 and window: 60 limits the tool to 10 calls per minute. Rate limits are tracked per agent session and reset automatically.
Set action: deny in the PolicyLayer policy for analyze_manifest. The AI agent will receive a policy violation error and cannot call the tool. You can also include a reason field to explain why the tool is blocked.
analyze_manifest is provided by the MCP server (@clarxai/mcp). PolicyLayer sits as a proxy in front of this server to enforce policies before tool calls reach the server.
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