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Glama

Search Evidence

search_evidence
Read-onlyIdempotent

Search library evidence across API, docs, examples, source, and release notes. Restrict by area or kind to find the specific information needed.

Instructions

Search a bounded set of API, docs, examples, source, or release evidence.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
areaNoRestrict to this namespace subtree.
kindsNoRestrict to evidence kinds; all but `source` when omitted.
queryYesWhat to look for.
cursorNoContinue a previous search. Same query, filters and snapshot.
max_bytesNoInline byte budget; the server caps it.
max_itemsNoPage size; the server caps it.
context_idYesFrom `resolve_library`.
snapshot_idNoRead a specific snapshot; the context's current one otherwise.

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
jobYes
dataYes
errorYes
statusYes
summaryYes
coverageYes
deliveryYesDelivery changes representation, never the original research status or coverage.
evidenceYes
artifactsYes
freshnessYes
context_idYes
request_idYes
snapshot_idYes
schema_versionYes

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.0.0

TDQS

B3.2/5.0
Behavior3/5

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

Annotations already cover readOnlyHint, idempotentHint, and closed-world behavior, so the safety profile is established. The description adds the evidence categories and the 'bounded set' limitation, which is useful, but it does not mention pagination, snapshot semantics, or default filtering 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 a single, front-loaded sentence with no wasted words. It clearly starts with the action 'Search' and immediately conveys the resource scope.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness2/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

For a tool with eight parameters, cursor-based pagination, snapshot semantics, and default kind filtering, the one-sentence description is too thin. It does not explain what bounds the set, how context_id relates to resolve_library, or the overall search model, so the agent must rely on the schema to understand operational details.

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%, so the schema already documents all eight parameters, including query, context_id, kinds, cursor, max_bytes, max_items, and snapshot_id. The description adds no parameter-level meaning, so the baseline score of 3 applies.

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 uses the specific verb 'search' and identifies a concrete resource: 'a bounded set of API, docs, examples, source, or release evidence.' This distinguishes it from siblings like inspect_symbol or read_artifact, though it does not explicitly contrast with them.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines2/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description provides no guidance on when to use this tool versus siblings such as verify_usage, inspect_symbol, or compare_releases. It states only what the tool does, leaving the agent to infer appropriate usage from the name and parameters.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.