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MCP Code Mode

MCP Code Mode

by anaseqal
OverviewSchemaSourceRelated ServersDiscussionsAdmin
Python
Local

get_learnings

Retrieve recorded learnings from past code executions. Filter by search term to find specific insights.

Instructions

View recorded learnings from past executions.

Args: search: Optional search term to filter learnings

Returns: Summary of learnings, optionally filtered

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

NameRequiredDescriptionDefault
searchNo

Output Schema

NameRequiredDescriptionDefault
resultYes

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.2.5

TDQS

A3.5/5.0
Behavior3/5

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

With no annotations, the description discloses it's a read-only operation ('View') and mentions optional filtering. However, it lacks detail on potential side effects or idempotency.

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?

The description is very concise, with three lines covering purpose, parameters, and returns. No fluff, main point first.

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

Completeness4/5

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

Given an output schema exists and the tool is simple, the description covers the core functionality. Missing details like pagination are minor for this tool.

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

Parameters4/5

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

Schema has 0% description coverage; the description adds meaning by explaining the 'search' parameter as an optional filter, overcoming the schema gap.

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

Purpose4/5

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

The description clearly states it views recorded learnings, using the verb 'View' and specifying the resource. It implicitly differentiates from sibling 'add_learning'.

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

Usage Guidelines2/5

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

No explicit guidance on when to use this tool versus alternatives like 'add_learning'. The description only states what it does, not when it's appropriate.

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

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