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get_api

Full detail for one API by aid (provider:api-slug); set include=["content"] to inline artifact bodies. Results carry next: the sub-resources that exist for this entity and the exact tool call that retrieves each, computed from this record. Pass include_next=false to omit it.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
aidYes
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
include_nextNoSet false to omit the `next` affordance block.
artifact_typesNo

TDQS

A3.7/5.0
Behavior3/5

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

With no annotations, the description must disclose behavior itself. It explains the `next` block (sub-resources and the exact tool call to retrieve each) and how to suppress it with include_next=false. This adds useful behavioral detail beyond a simple 'returns details'. However, it does not mention any side effects, error conditions, or authorization requirements, which are expected for a full-detail getter. It's a middle ground.

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?

Two sentences with no fluff. The first sentence states the core purpose and the key option, the second explains the `next` affordance and its toggle. Information is front-loaded and every phrase earns its place, making it 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?

For a 5-parameter tool with no output schema, the description covers the main purpose and two primary parameters but omits `context` and `artifact_types`. It also doesn't describe the overall return structure beyond the `next` block, which is a notable gap given there's no output schema. It's sufficient for a basic call but not fully complete for edge cases.

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 only 40%, so the description must compensate. It does clarify `aid` (provider:api-slug), `include` (inline artifact bodies), and `include_next` (omit next block). However, `context` and `artifact_types` are left unexplained, leaving two parameters with no additional meaning. The description adds some value but does not fully cover the parameter list.

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 opens with a clear verb and resource: 'Full detail for one API by aid (provider:api-slug)'. It specifies the identifier format and distinguishes this from sibling search tools like find_apis by stating it retrieves a single API's full detail. The purpose is unambiguous and differentiates this getter from other getters like get_api_artifacts or get_openapi.

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 gives usage instructions for include and include_next, but does not explicitly state when to prefer this tool over siblings. For example, no mention that get_api_artifacts might be better for artifacts alone, or that find_apis is for search. The context is clear but lacks exclusions or alternative-tool routing, so it's adequate but not proactive.

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