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Github Search

github_search
Read-only

GitHub search with its own query syntax (e.g. 'sqlite language:rust stars:>100'). Repositories: name, owner, stars, forks, language, topics, licence, pushed date. Issues: title, state, labels, comments, URL. Code: path, repository and link. GitHub limits search to about 30 requests a minute; a busy minute is a 503, not charged. Limit max 30. Price: $0.002 in USDC per successful call, paid with x402 (Base, Solana or Arc); failed calls are not charged.

Input Schema

NameRequiredDescriptionDefault
qYesGitHub search query, GitHub search syntax (1-256 characters)
typeNorepositories (default), issues, code or users
limitNoResults, 1-30 (default 10)

Output Schema

NameRequiredDescriptionDefault
typeNo
countNo
queryNo
sourceNo
resultsNo
checkedAtNo
totalCountNo
incompleteResultsNo

Schema Changelog

Changes observed during successful MCP inspections.

  1. Added

TDQS

A4.6/5.0
Behavior5/5

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

Annotations provide readOnlyHint and openWorldHint, but the description adds substantial context: rate limit (~30 req/min), 503 on busy minutes (not charged), result limit max 30, pricing model ($0.002 USDC per successful call), payment rails (Base, Solana, Arc), and failed calls are not charged. This is unusually rich behavioral disclosure beyond annotations.

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

Conciseness4/5

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

Dense but well-organized: purpose, return fields per type, rate limit, pricing. No wasted words. However, the pricing/rate-limit details are somewhat interleaved with functional description; could be slightly more front-loaded.

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

Completeness5/5

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

Given the tool's complexity (external API, rate limits, payment), the description covers everything an agent needs: what it searches, return shape, limits, cost, and failure behavior. Output schema exists, so return value explanation is not required, but the field enumeration is a bonus.

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 coverage is 100%, so baseline is 3. The description adds meaningful detail: query syntax example, the fact that limit is capped at 30 (matching schema), and enumerates the return fields per type. It doesn't add new parameter constraints beyond schema, but the syntax example is practical value.

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?

States a specific verb (search) and resource (GitHub) and enumerates the entity types returned (repositories, issues, code). The example query with syntax makes the scope unmistakable. Sibling tools github_issues, github_repo, github_user exist, and the description implies this is the generic multi-type search, though it doesn't explicitly route users away from the type-specific siblings.

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?

Clearly establishes context: a unified GitHub search with query syntax. However, it does not explicitly say when to use this vs github_issues, github_repo, or github_user, which are obvious alternatives. The 'type' parameter implicitly covers generality but no exclusion guidance is offered.

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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