Skip to main content
Glama
elad12390

Web Research Assistant

by elad12390

api_docs

Dynamically discovers and retrieves official API documentation with examples and explanations. Works for any API by searching or guessing common doc URLs.

Instructions

Search and fetch official API documentation with examples and explanations.

Documentation-first approach: fetches human-written docs with context, examples,
and best practices. Much more useful than OpenAPI specs alone.

Discovery strategy:
1. Try common URL patterns (docs.{api}.com, {api}.com/docs, etc.)
2. If patterns fail, search for "{api} API official documentation"
3. Crawl discovered docs and extract relevant content

No hardcoded URLs - works for ANY API by discovering docs dynamically.

Examples:
- api_docs("stripe", "create customer", reasoning="Setting up payments")
- api_docs("github", "create repository", reasoning="Automating repo creation")
- api_docs("spartan", "button component", reasoning="Learning UI library")

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
api_nameYes
reasoningYes
topicYes
max_resultsNo

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes
Behavior4/5

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

The description discloses the dynamic discovery process (common URL patterns, search fallback, crawling) and notes no hardcoded URLs. With no annotations, this adds valuable behavioral context, though details like rate limits or auth are absent.

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 well-structured with front-loaded purpose, followed by comparison, strategy, and concise examples. Every sentence adds value without redundancy.

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?

Given the tool's complexity, the description covers the key aspects: purpose, strategy, and examples. The presence of an output schema reduces the need to detail return values, making this sufficiently complete for an AI agent.

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 description and examples illustrate the role of 'api_name' and 'topic' and mention default for 'max_results'. But it does not fully explain the 'reasoning' parameter, and schema coverage is 0%, leaving some ambiguity.

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's purpose as searching and fetching official API documentation, distinguishing it from generic web searches or scraping. It explicitly contrasts with OpenAPI specs, emphasizing its value for human-written docs.

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 provides a documented discovery strategy and examples, guiding when to use the tool (e.g., for any API's official docs). However, it lacks explicit when-not-to-use guidance or alternatives.

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

Install Server

Other Tools

Latest Blog Posts

MCP directory API

We provide all the information about MCP servers via our MCP API.

curl -X GET 'https://glama.ai/api/mcp/v1/servers/elad12390/web-research-assistant'

If you have feedback or need assistance with the MCP directory API, please join our Discord server