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find_postman

Postman collections across the catalog. Postman-format collections (by reference). Filter by q / tags / providers; include=["content"] inlines bodies. Use find_artifacts for cross-type search.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
qNoFree text over name + description.
pageNo
tagsNoTag slugs.
limitNo
matchNoany
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.
includeNo
providersNo

TDQS

A3.9/5.0
Behavior3/5

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

No annotations are provided, so the description carries the full burden. It discloses that results are 'by reference' (not bodies) unless include=['content'] is passed, which is a useful behavioral trait. It also implies a read-only search operation, consistent with the 'find' prefix. However, it does not describe return format, pagination behavior (though page/limit exist), or how results are ordered/ranked. For a tool with zero annotations, more transparency about defaults and output structure would be expected.

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, front-loaded with the primary purpose and a clear alternative. Every clause contributes: the first sentence states what the tool does, the second gives filtering options and a pointer to find_artifacts. There is no redundant or vague wording; it is highly efficient.

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

Completeness3/5

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

This is a moderately complex tool with 8 parameters and no output schema or annotations. The description covers the core filter options and the include behavior, and routes to find_artifacts for cross-type search. However, it omits details on pagination defaults (page/limit), the match parameter semantics, and what 'by reference' implies for the return payload (e.g., IDs only). Given the low schema coverage, these gaps make the description insufficient for a fully confident invocation without additional exploration.

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 low at 38% (only q, tags, and context have descriptions). The description compensates by explaining that q/tags/providers are filters and that include=['content'] inlines bodies. This adds meaning beyond the schema for providers and include. However, page, limit, match, and providers are not elaborated in the description, and only the schema covers context. The description does not fully compensate for the low schema coverage, though it clarifies the most important query-related parameters.

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 clearly states it finds Postman collections across the catalog, specifying 'Postman-format collections (by reference)'. It distinguishes itself from find_artifacts for cross-type search, and implicit sibling differentiation is strong because the collection type is specific. The verb 'find' and resource 'Postman collections' are precise.

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 explicitly says 'Use find_artifacts for cross-type search', which provides an alternative for when this tool is not appropriate. It also mentions the filter capabilities (q/tags/providers) and the include option, giving context on how to use it. However, it does not explicitly delineate when to use this over other find_* tools like find_collections or find_openapis, so some exclusions are implicit.

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