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Glama

check_status

INFLUENCE — the status of a check request by id: queued, in_review, running, done, rejected or needs_info, with any notes shared by the reviewer. The id identifies the request, but the Influence plan is now required to read it back. To report an error for free, use report_correction or open an issue on the provider's api-evangelist repo.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
idYes
contextNoOptional: why you are asking. One sentence — the task you are trying to complete, or what you expect to get back. Never included in the answer and never used to rank; it is read only when a result turns out to be wrong, which is when knowing the intent is what makes the report actionable.

TDQS

A3.6/5.0
Behavior4/5

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

With no annotations, the description carries the full burden and does a reasonably good job: it says 'read it back,' lists possible status values, mentions reviewer notes, and covers the plan requirement and error-reporting path. It does not explicitly rule out side effects or mention rate limits, but for a status check this is fairly transparent.

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 description is only three sentences and packs in useful information, but the cryptic 'INFLUENCE —' opener and the awkward 'now required' phrasing detract from clarity. It is structured competently but not front-loaded around a clean verb phrase.

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?

Since there is no output schema, listing the six possible statuses and notes meaningfully fills the gap. The description also covers the plan prerequisite and how to report errors; missing details like id provenance or invalid-id behavior are secondary for a simple status read.

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 schema describes 'context' well, but 'id' has no schema description. The tool description only adds 'the id identifies the request,' which is minimal and doesn't explain id format or where to obtain it, so it only partially compensates for the 50% schema coverage.

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 resource ('a check request by id') and the returned data: the status values and any reviewer notes. It also signals the read nature with 'read it back,' though the leading 'INFLUENCE —' is confusing and no explicit verb like 'retrieves' is used.

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?

It provides some usage context: the Influence plan is required, and errors should be routed to report_correction. However, it never explicitly says when to use this tool over siblings like request_check or my_checks, leaving the usage conditions mostly implied.

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

B3.1/5.0
Disambiguation3/5

Most tools are clearly separated by artifact type or resource (find_mcp vs find_openapi vs get_provider vs get_api), but the sheer volume creates some genuinely confusable clusters: apis_io_search vs find_apis vs find_artifacts, and insights_adoption vs insights_dimensions vs find_company_insights. Several readiness-related tools (what_can_i_fix, simulate_fixes, readiness_gates) also share a conceptual boundary, though their descriptions do help.

Naming Consistency3/5

The dominant patterns (find_*, get_*, cohort_*, compare_*) are consistent and predictable, but the set mixes in irregular names like apis_io_search, tag_group_tags, what_can_i_fix, whats_changed, and resolve. These deviations are readable but break the otherwise regular verb_noun convention.

Tool Count2/5

106 tools is far beyond the typical well-scoped server and will impose a heavy selection burden on agents. The server covers a genuinely broad domain (catalog search, ratings, cohorts, agent readiness, lists, exports, feedback), so the count is defensible in scope, but it is still too many to navigate efficiently.

Completeness5/5

The surface is remarkably complete: search and browse, single-entity detail, comparisons, cohort analytics, agent-readiness assessment, saved searches, list management, feedback/correction flows, and full dataset exports are all covered. There are no obvious dead ends, and even minor operations like re-running saved searches or simulating fixes are present.

Resources