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scan_repository

Read-onlyIdempotent

Statically audit a public GitHub repository's MCP tool definitions for destructive operations, annotation mismatches, injection surfaces, and credential exposure. Returns structured risk findings without cloning or executing code.

Instructions

Audit a public GitHub repository's MCP tool definitions and return structured findings. Extracts tools from TypeScript, JavaScript, Python and Go source, then applies seven rules covering unguarded destructive operations, annotation mismatches, injection surface in tool descriptions, hidden instructions aimed at automated systems, personal data in parameters, credentials in parameters, and unbounded scope. Read-only: clones nothing, executes nothing, and never calls tools/call on the audited server. Operates only on the repository named in the argument.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
min_severityNoOmit findings below this severity. Defaults to low.
repository_urlYesA github.com repository URL, e.g. https://github.com/owner/name

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.3.0

TDQS

A4.2/5.0
Behavior5/5

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

Goes well beyond the annotations by enumerating the seven audit rules, the source languages parsed, and explicit non-execution guarantees ('clones nothing, executes nothing, and never calls tools/call'), which is exactly the behavioral context an agent needs for a read-only scanner whose annotations already cover the safety profile.

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?

Front-loads the purpose, then the rule set, then the safety guarantees in three sentences with zero filler; the length is justified by the breadth of what the audit covers.

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?

With no output schema, 'return structured findings' is slightly thin on the return shape, but the enumeration of the seven rule categories gives the agent a good sense of what findings will contain. Complete enough for correct invocation.

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%, so both parameters are already documented in the schema, including the enum for min_severity. The description reinforces the repository_url scope but adds no format or filtering 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 ('Audit a public GitHub repository's MCP tool definitions') and immediately narrows scope to the repo in the argument, which cleanly separates it from siblings like scan_tool_manifest and explain_rule.

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?

Usage is implied by the scoping sentence ('Operates only on the repository named in the argument'), but there is no explicit when-to-use guidance and no mention of the alternatives scan_tool_manifest or explain_rule, leaving the routing decision to inference.

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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