cc-agent

get_learnings

Retrieve accumulated learnings from prior agents for a repository or namespace to understand past discoveries and insights.

Instructions

Return accumulated learnings for a repo or namespace. Learnings are written by agents at the end of each job. Use this to understand what prior agents have discovered.

Tool call template

This tool declares no required parameters. Add optional parameters from the input schema as needed.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {},
    "name": "get_learnings"
  }
}

Input Schema

Name	Required	Description
`repo`	No	Repo key to query, e.g. 'gonzih/cc-agent'. Takes precedence over namespace when provided.
`limit`	No	Maximum number of learnings to return (default 10)
`namespace`	No	Namespace to query (fallback when repo is not provided; defaults to current namespace)

Schema Changelog

Changes observed during successful MCP inspections.

First observedv0.16.15Jul 30, 2026

TDQS

A3.9/5.0

Behavior3/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

With no annotations, the description bears full burden. It discloses that learnings are written by agents at end of each job, implying a read-only query. But it lacks details on authorization, rate limits, or data freshness. Adequate but not thorough.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

Two sentences with zero wasted words. The purpose and usage hint are front-loaded, making it easy to scan.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness3/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

No output schema exists, so description should hint at return format. It states 'accumulated learnings' but does not describe structure (e.g., array of objects with fields). Nonetheless, the tool is simple and the description suffices for basic understanding.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters3/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema coverage is 100% with parameter descriptions already provided. The description adds no new parameter semantics beyond restating 'repo or namespace'. Baseline score of 3 is appropriate.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

Description clearly states the verb 'return', resource 'accumulated learnings', and scope 'repo or namespace'. It distinguishes from sibling 'clear_learnings' by indicating this is a retrieval operation.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines4/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

Explicitly states 'Use this to understand what prior agents have discovered', giving a clear when-to-use context. However, it does not mention when not to use or exclude alternatives like 'clear_learnings'.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.