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Glama

Apis Guru

by io.github.pipeworx-io

Get Api

get_api
Read-onlyIdempotent

Get the full directory entry for one API by its exact APIs.guru name/key (e.g. "stripe.com" or "googleapis.com:calendar"). Returns every version of that API with its info (title, description, provider, categories), last-updated date, docs link, and OpenAPI/Swagger spec URLs (swaggerUrl JSON + swaggerYamlUrl YAML), plus which version is preferred. Use this once you know the exact name (from search_apis) to fetch the spec URLs for an API.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
nameYesExact APIs.guru directory key, e.g. "stripe.com" or "googleapis.com:calendar".

Schema Changelog

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

  1. Changed1 schema field changed
    • addedInput schema / examples
      Added value: +[
+  {
+    "name": "stripe.com"
+  },
+  {
+    "name": "googleapis.com:calendar"
+  }
+]
  2. First observed

TDQS

A4.7/5.0
Behavior4/5

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

Annotations already indicate read-only, open world, idempotent, and non-destructive behavior. The description adds valuable details about what is returned (every version, info, last-updated, spec URLs, preferred version), which is consistent with the annotations.

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: first explains the action with examples, second provides usage context and return summary. No wasted words, front-loaded with key information.

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

Completeness5/5

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

Despite no output schema, the description thoroughly explains the return value (versions, info, spec URLs, preferred version). Combined with annotations and simple schema, it is complete for the tool's purpose.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters4/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

With 100% schema coverage, baseline is 3. The description reinforces the parameter meaning by giving examples and context ('exact APIs.guru name/key', 'from search_apis'), adding value beyond the schema.

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 the tool retrieves the full directory entry for one API by its exact APIs.guru name/key. It distinguishes itself from sibling tools like search_apis by specifying that this is used after obtaining the exact name.

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

Usage Guidelines5/5

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

Explicitly instructs to use this tool once the exact name is known from search_apis, and the goal is to fetch spec URLs. This provides clear when-to-use and when-not-to-use guidance.

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

A4/5.0
Disambiguation3/5

Several tools have overlapping purposes—ask_pipeworx, ask_pipeworx_beta, and ask_pipeworx_grounded are near-twins (beta currently matches the stable router exactly), and deep_research, entity_profile, recent_changes, and compare_entities all fan out across similar data sources. The long descriptions do help differentiate them, but an agent selecting quickly could easily pick the wrong variant.

Naming Consistency3/5

Most tools use snake_case, but the verb style is inconsistent: get_api, list_providers, and validate_claim use verb_noun, while remember/forget/recall are bare verbs and polymarket_arbitrage, entity_profile, and bet_research are noun phrases. The pattern is readable but not predictable enough to infer behavior from the name alone.

Tool Count2/5

At 35 tools, the surface is heavy, and many are hyper-specialized (five separate Polymarket tools, three ask_pipeworx variants, three memory tools). The breadth is defensible for a multi-domain data platform, but it goes past the comfortable 16-25 range and would benefit from consolidation.

Completeness4/5

The tool set covers the main research lifecycle well: discovery, routing, grounded answers, entity resolution, profiling, comparison, claim validation, change tracking, subscription management, and memory. Minor gaps exist—such as no explicit fetch-by-citation-URI tool and soft-failed patent coverage—but agents can generally work around them.

Resources