TestGraph

Search reviews and known subjects

search

Read-onlyIdempotent

Search reviews plus matching reviewed or unreviewed subjects. Search is lexical rather than semantic: for an ordinary question try one discriminating keyword at a time, then exact subject-name follow-ups and fetch every returned review. Continue with next_cursor until has_more is false before claiming exhaustive retrieval. Never merge records by display name: group and compare using subject_id and subject_type because unrelated subjects may share a name. Known subjects include immediate subject-to-subject connections so a location, organisation, variant or sibling discovered earlier can inform recommendations without being misrepresented as reviewed. For a location-based recommendation, do not stop when the target-town query has no direct result: also search the relevant subject type without a text query, follow reviewed subjects to parent organisations, and inspect each parent's official branch directory for the requested location before concluding there is no useful connection. Search returns collection_coverage on collection subjects and connected parents. Only coverage_status=complete permits a conclusion that a location or member is absent; partial or unknown coverage must be reported as uncertainty. Routine chain expansion does not require user confirmation. Search is lexical rather than semantic. For an ordinary user question, try one discriminating keyword at a time and retry with a subject-type-only search when necessary. A keyword hit is only a discovery step: search each candidate's exact subject name, then fetch every returned review before answering so reviews that omit the original keyword are not missed. Retrieval is deliberately softer than canonical naming. Search using the user's wording first, then try known aliases, canonical type names and useful broader/related types when needed. A search miss for one label is not evidence that the underlying subject or concept is absent. Stable IDs, not preferred labels, determine identity.

Input Schema

TableJSON Schema

Name	Required	Description
`limit`	No
`query`	No
`cursor`	No	Opaque next_cursor returned by the preceding identical search.
`subject_type`	No
`include_related`	No

Schema Changelog

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

First observedAug 23, 2026

TDQS

B3.4/5.0

Behavior1/5

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

The description contradicts the annotation openWorldHint=false. The annotation signals a closed-world tool where absence in results is meaningful, but the description explicitly says 'A search miss for one label is not evidence that the underlying subject or concept is absent' and only coverage_status=complete permits concluding absence. This is an annotation contradiction that overrides any otherwise useful behavioral disclosure.

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

Conciseness3/5

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

The definition is front-loaded with the core purpose and contains genuinely valuable detail, but it is noticeably repetitive: 'lexical rather than semantic' appears more than once and the 'one keyword, then exact search, then fetch' instruction is restated. It is acceptable but not tight.

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?

For a tool with no output schema, this description generously covers the main contract: what is searched, why keyword misses are unreliable, cursor/has_more pagination, collection_coverage semantics, and the special location-recommendation procedure. It lacks a full return-shape description, but the coverage and example constraints make it largely sufficient.

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?

With schema description coverage at only 20%, the description adds useful meaning for query, subject_type, and cursor, such as keyword-first search, type-only fallback, and pagination. However, it gives no explicit clarification of limit or include_related semantics, so parameter-level compensation is only partial.

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 opening sentence names a concrete verb and resource: 'Search reviews plus matching reviewed or unreviewed subjects.' It also clarifies lexical rather than semantic search and the subject/review scope, making it distinguishable from sibling tools like fetch or the submission/assertion 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?

The description gives operational guidance: start with one discriminating keyword, follow up with exact subject-name searches, fetch every returned review, paginate until has_more is false, and use coverage_status to determine absence. It does not explicitly contrast this tool with sibling alternatives, but its usage context is otherwise strong.

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

A3.8/5.0

Disambiguation4/5

Tools cluster into clear functional families — classification, location, reviews, deliberations, and system — and potentially overlapping operations are explicitly cross-referenced (e.g., enrich_subject vs correct_subject_fact, affirm vs propose reclassification). The main hazard is the resolve_subject / resolve_subject_type / resolve_subject_hierarchy trio, whose near-identical prefixes could mislead an agent at first glance despite well-written descriptions.

Naming Consistency4/5

The surface is dominated by a consistent snake_case verb_noun pattern with stable verb families: get_*, list_*, resolve_*, set_*, save_*, create_*, register_*, and submit_*. Minor deviations — bare-verb fetch and search, and the noun-led vocabulary_index — break the pattern slightly but do not obscure it.

Tool Count3/5

34 tools is heavy and above the preferred range, and the classification family alone accounts for ten tools with substantially duplicated vocabulary guidance. The count is partially earned, however, because the server genuinely spans several subsystems — reviews, subject classification, location assertions, deliberations, and governance — each with its own lifecycle.

Completeness4/5

Each subsystem has thorough lifecycle coverage: deliberations (create/claim/contribute/get/list/resolution), reviews (save/fetch/delete/list/visibility), location (assert/list/resolve), and classification (propose/affirm/reopen/relationships/aliases). Minor gaps include no way to edit review content, no direct list-all-subjects endpoint, and no retirement path for fields or aliases.

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