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CartographAI

Atlas Docs MCP Server

by CartographAI

get_docs_index

Retrieves a condensed, LLM-friendly index of documentation pages, returned as a markdown list. Use it to identify relevant pages before requesting full content.

Instructions

Retrieves a condensed, LLM-friendly index of the pages in a documentation set. Use this for initial exploration to understand what's covered and identify relevant pages. Returns a markdown page with a list of available pages. Follow up with get_docs_page to get full content.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
docNameYesName of the documentation set
Behavior4/5

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

With no annotations provided, the description carries the behavioral disclosure burden. It discloses that the tool returns a markdown page containing a list of available pages, and that the result is condensed and LLM-friendly. It does not discuss edge cases or errors, but for a simple read-oriented tool this is reasonably transparent.

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?

Three sentences, all of which carry signal: first states the action and resource, second gives usage guidance, third describes the return format and next step. No filler or redundant phrasing.

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?

For a single-parameter tool with no output schema, the description covers purpose, usage context, return shape, and a follow-up path. An agent has enough information to invoke it correctly and interpret the result.

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 description coverage is 100%, so the schema already fully documents the only parameter, docName. The description does not add additional meaning about the parameter beyond referencing the documentation set, so the baseline score of 3 is appropriate.

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 uses a specific verb ('Retrieves') and a specific resource ('condensed, LLM-friendly index of the pages in a documentation set'), clearly distinguishing this from sibling tools like get_docs_page or get_docs_full. It also frames its role as an exploration entry point, which separates it from search_docs and list_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?

It explicitly states when to use this tool ('for initial exploration to understand what's covered') and suggests a follow-up action ('Follow up with get_docs_page to get full content'). It does not explicitly mention when not to use it or compare it against all siblings, but the usage context is clear.

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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