embed_repo

SERVERTrace SOURCEnikolai-vysotskyi/trace-mcp

Low RISK CLASS

Category Read

Parameters 00 required

Recommended Allowedsee the rule below

Registry record Grade F, identity unverified Pull the record →

This record as markdown: /tools/nikolai-vysotskyi-trace-mcp/embed-repo.md

WHAT IT DOES

What embed_repo does on Trace

AI agents call embed_repo to retrieve information from Trace without modifying anything. It is typically the context-gathering step in research, monitoring, and reporting workflows, before the agent takes action elsewhere.

RISK

Why embed_repo is rated Low

Even though embed_repo only reads data, uncontrolled read access leaks sensitive information and racks up API costs: an agent caught in a retry loop can make thousands of calls a minute without anyone noticing.

Attacks that exploit this kind of access

RECOMMENDED RULE

The rule that runs embed_repo safely

PolicyLayer is an MCP gateway: it sits between your AI agents and Trace, and checks every tool call against a rule you set before the call runs. Nothing changes on the server itself. For embed_repo, this is the rule to start with:

embed_repo Allowed

embed_repo is read-only, so it stays allowed. Everything else on the server is denied unless you say otherwise.

View as policy code

policy.json

{
  "version": "1",
  "default": "deny",
  "tools": {
    "embed_repo": {}
  }
}

ALLOW ONLY THIS TOOL → Instant setup, no code required.

The button opens the PolicyLayer dashboard: create your workspace, connect Trace, apply this rule, and every embed_repo call is checked against it from then on.

FAQ

Questions about embed_repo

What does the embed_repo tool do? +

Precompute and cache symbol embeddings for semantic / hybrid search. Also computed lazily on first semantic query — call this once after a fresh index to avoid that latency spike. Requires an AI provider (ollama/openai) enabled in config. force=true recomputes all embeddings. Mutates the vector store; idempotent. Returns JSON: { status, indexed_this_run, total_embedded, coverage_pct, duration_ms }. Failed batches return status. It is categorised as a Read tool in the Trace MCP Server, which means it retrieves data without modifying state.

How do I enforce a policy on embed_repo? +

Register the Trace MCP server in PolicyLayer and add a rule for embed_repo: 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 Trace. Nothing to install.

What risk level is embed_repo? +

embed_repo is a Read tool with low risk. Read-only tools are generally safe to allow by default.

Can I rate-limit embed_repo? +

Yes. Add a rate_limit block to the embed_repo 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.

How do I block embed_repo completely? +

Set action: deny in the PolicyLayer policy for embed_repo. 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.

What MCP server provides embed_repo? +

embed_repo is provided by the Trace MCP server (nikolai-vysotskyi/trace-mcp). PolicyLayer sits as a proxy in front of this server to enforce policies before tool calls reach the server.

KEEP EXPLORING

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