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Inspect an MCP server

inspect_mcp
Read-onlyIdempotent

Inspect a specific MCP server by ID from find_mcp: review upstream claims, deployments, Pod-observed tools, and decision-useful GitHub issues, keeping firsthand observations separate.

Instructions

Inspect one canonical MCP from find_mcp, including distinct upstream claims, deployments, Pod-observed tools, and selected decision-useful GitHub issues. GitHub reports remain separate from firsthand Pod observations.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
idYesExact canonical MCP ID returned by find_mcp.
queryNoOptional problem or question used to rank and filter issue reports.
issueLimitNoMaximum decision-useful GitHub issues to return, from 1 to 20.

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultNoCanonical MCP record. Omitted when the ID is unknown or is not an MCP.
interactionIdYesStable ID for this inspection, used when sending Pod feedback.

Schema Changelog

Changes observed during successful MCP inspections. Dates show when Glama detected each change.

  1. First observedv0.1.1

TDQS

A4.3/5.0
Behavior4/5

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

Annotations already cover read-only, idempotent, and non-destructive behavior. The description adds meaningful behavioral context by disclosing that GitHub reports remain separate from firsthand Pod observations, indicating how data sources are kept distinct and how the tool treats provenance.

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 two sentences with no filler. The core action and source are front-loaded, and the second sentence adds an important provenance distinction without redundancy.

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 read-only nature, full parameter schema, rich annotations, and presence of an output schema, the description is complete enough for correct invocation. It states the required input source, the content that will be inspected, and the separation of data provenance.

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?

The input schema already provides 100% coverage for all three parameters, including the UUID constraint for id, the ranking/filtering purpose of query, and the 1–20 range for issueLimit. The description adds no parameter-specific detail beyond what the schema already states, so the baseline of 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?

The description uses a specific verb ('Inspect') with a clear resource ('one canonical MCP from find_mcp') and enumerates the exact content returned: upstream claims, deployments, Pod-observed tools, and decision-useful GitHub issues. This makes it easily distinguishable from find_mcp and the other sibling tools.

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?

It clearly implies the workflow: first use find_mcp to obtain a canonical MCP ID, then inspect that server. It does not explicitly name alternatives or say when not to use the tool, but the 'from find_mcp' context is strong enough for an agent to infer the right situation.

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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