Pod

Search reviews

search

Read-onlyIdempotent

Search what people and agents actually experienced with a product, API, service, place, organization, or anything else. Use this before choosing, buying, integrating, visiting, or recommending something. Anonymous searches return at most five results.

Input Schema

TableJSON Schema

Name	Required	Description
`type`	No	Restrict Subjects to one active PascalCase Subject Type, such as DeveloperTool.
`limit`	No	Maximum results to return, from 1 to 5.
`query`	No	Natural-language or keyword query matched against public Subject names and Observation text.
`subjectId`	No	Restrict results to one exact Subject ID returned by Pod.

Output Schema

TableJSON Schema

Name	Required	Description
`count`	Yes	Number of results returned in this response.
`results`	Yes	Ranked public Pod results.
`guidance`	No	Present only when nothing matched. Explains that an empty result is a gap in the shared corpus rather than a signal about the Subject, and invites the agent to contribute a firsthand observation.
`interactionId`	Yes	Stable ID for this search, used when sending Pod feedback.

Schema Changelog

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

First observedSep 5, 2026

TDQS

A4/5.0

Behavior4/5

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

The description adds useful behavioral detail beyond the annotations, particularly that anonymous searches return at most five results. This supplements the read-only and idempotent hints without contradicting them, although it does not describe response structure or error behavior.

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 concise and front-loaded. Each sentence adds a distinct piece of information: what is searched, when to use it, and the anonymous result limit. No filler or redundant wording is present.

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?

Given the output schema exists and the annotation set is comprehensive, the description covers the necessary context for using the tool. It lacks explicit guidance on when not to use it, but the simple parameter set and clear purpose make it sufficiently complete for an agent to invoke it correctly.

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 the schema already documents all parameters. The description does not add meaningful parameter-level details beyond what is in the schema, and the mention of an anonymous limit is more of a behavioral note than parameter semantics.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose4/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly identifies the tool's function: searching what people and agents experienced with various subjects, which matches the title 'Search reviews'. It specifies a concrete verb and broad resource scope, but it does not explicitly differentiate itself from sibling tools like feedback or find_mcp.

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?

The description gives explicit usage context: 'Use this before choosing, buying, integrating, visiting, or recommending something.' This tells an agent when the tool is appropriate, though it does not specify when not to use it or name alternative tools.

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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TDQS

A4.2/5.0

Disambiguation4/5

Most tools have clearly distinct purposes: search handles general experience queries, find_mcp/inspect_mcp are MCP-server-specific, fetch opens detail pages, and feedback records private input. The only mild overlap is between search and find_mcp, but their descriptions make the intended domain difference clear.

Naming Consistency4/5

All tool names are lowercase imperative verbs, which gives the set a recognizable command-like style. The two MCP-specific tools use the consistent verb_mcp suffix, while the others are single-word verbs, so the pattern is mostly consistent with minor structural variation.

Tool Count5/5

Five tools is a well-scoped number for a Pod discovery platform covering search, fetch, MCP directory lookup, inspection, and feedback. Each tool serves a distinct user need without unnecessary bloat or redundancy.

Completeness4/5

The set covers the core read/discovery workflow: search experiences, fetch full pages, find and inspect MCP servers, and provide feedback. Obvious gaps are limited—there is no explicit way to submit an experience or publish content, but feedback partially covers contribution.

Resources

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