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Crypto Bot Audit + Market Data (x402 paid)

GitHub repository health

market_github_repo_health
Read-onlyIdempotent

Keyless read of the public GitHub API for one repository: archived, forked, how long since the last push, open issue count, stars, license, topics — plus the flags an integrator actually acts on (archived, no license, unmaintained window). Does not read the source, the dependency tree, or anything private, and does not clone or build. Costs $0.001 USDC per call. Byte-identical to GET /market/github-repo-health?owner=modelcontextprotocol&repo=registry.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
repoYesrepository name [required]
ownerYesrepository owner or org [required]

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
tsNo
forkNo
noteNo
riskNo
flagsNo
foundNo
staleNo
caveatNo
reasonNo
sizeKbNo
sourceNo
topicsNo
archivedNo
fullNameNo
pushedAtNo
createdAtNo
openIssuesNo
stargazersNo
licenseSpdxNo
defaultBranchNo
lastPushAgeDaysNo

Schema Changelog

Changes observed during successful MCP inspections.

  1. Added

TDQS

A3.9/5.0
Behavior4/5

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

Annotations already declare readOnlyHint=true, openWorldHint=true, idempotentHint=true and destructiveHint=false, so the safety profile is covered. The description adds value beyond that: it discloses cost ($0.001 USDC per call), that access is keyless, and the precise read boundaries. It does not cover rate limits, which keeps it from a 5.

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?

The output fields, the scoping exclusions, and the cost are all front-loaded and dense. The trailing 'Byte-identical to GET /market/...' sentence is useful for integrators but slightly redundant with the rest; overall it remains efficient with little waste.

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?

An output schema exists, so return-value detail is not required and the description appropriately focuses on scope, cost, and boundaries. Combined with full annotation coverage and complete schema documentation, an agent has what it needs, though a note on rate limits or auth prerequisites would round it out.

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 description coverage is 100% for the two parameters (owner, repo), so the schema already documents their meaning. The description embeds an example URL carrying owner/repo values but adds no syntax or constraint detail beyond the schema, so the baseline 3 applies.

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 and resource ('Keyless read of the public GitHub API for one repository') and enumerates the exact fields returned (archived, forked, last push, open issues, stars, license, topics) plus the actionable flags. An agent can tell this apart from sibling market_* tools without opening the schema.

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 scopes the tool well ('Does not read the source, the dependency tree, or anything private, and does not clone or build'), which implies when it applies. However, it never names an alternative tool or an explicit when-to-use/when-not condition, so usage is only implied rather than directed.

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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