Apollo

Search Repo Implementations

search_repo_implementations

Find verified open-source GitHub repositories for code implementations and architectural patterns. Get clean, license-free snippets ready to use.

Instructions

Search verified open-source GitHub repositories for code implementations and architectural patterns. Strips license boilerplate and formats clean implementation snippets.

Tool call template

Replace each placeholder with a value matching the input schema. Only required parameters are included; add optional parameters as needed.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {
      "topic": "<topic>"
    },
    "name": "search_repo_implementations"
  }
}

Input Schema

Name	Required	Default
`top_k`	No
`topic`	Yes
`language`	No	python
`min_stars`	No

Output Schema

Name	Required	Description	Default
`result`	Yes

Schema Changelog

Changes observed during successful MCP inspections.

First observedv0.1.0Sep 2, 2026

TDQS

B3.3/5.0

Behavior3/5

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

With no annotations, the description carries the behavioral disclosure burden. It adds meaningful non-obvious behavior: stripping license boilerplate and formatting clean implementation snippets. However, it omits important context such as what 'verified' means, how results are ranked, or any rate or scope limitations.

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 compact and front-loaded, with two distinct sentences that each add value: one for purpose and one for output processing. There is no fluff, repetition, or extraneous detail.

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

Completeness2/5

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

The presence of an output schema covers return-value structure, but the description still needs to explain parameter semantics, verification criteria, and when this tool is preferred over siblings. Since the schema provides no parameter descriptions and annotations are absent, the current description leaves essential usage context missing.

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

Parameters1/5

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

The input schema has 0% description coverage across all four parameters, and the description does not compensate by explaining topic, top_k, language, or min_stars. An agent gets no guidance on how to set these parameters or what defaults mean.

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?

The description states a specific verb ('Search'), a concrete resource ('verified open-source GitHub repositories'), and a clear intent ('code implementations and architectural patterns'). This distinguishes it from the sibling tools, which target academic papers and general web search.

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

Usage Guidelines3/5

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

The description implies the tool is for finding code implementations in GitHub repositories, but it never explicitly states when to use this tool versus alternatives, nor does it mention exclusions or when not to use it. The context signals hint at alternative tools, but the description itself provides no direct routing guidance.

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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