Skip to main content
Glama
Sign In

Citehound

by io.github.andacuzel
OverviewDiscussionsAdmin

Generate an llms.txt file

generate_llms_txt
Read-onlyIdempotent

Builds an llms.txt file — an optional convention for describing a site and its key pages, with no demonstrated effect on Google — from a name, a one-sentence description and a list of key pages. Pure text generation — fetches nothing.

Tool call template

Replace each placeholder with a value matching the input schema. Only required parameters are included; add optional parameters as needed.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {
      "name": "<name>",
      "description": "<description>"
    },
    "name": "generate_llms_txt"
  }
}

Input Schema

NameRequiredDescriptionDefault
nameYesThe site or product name for the llms.txt header.
key_pagesNoPages worth pointing an AI system to, most important first.
descriptionYesA one-sentence summary of what the site is.

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observed

TDQS

A3.8/5.0
Behavior4/5

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

Annotations already cover read-only, idempotent, non-destructive, closed-world, so the bar is lower. The description still adds value: 'Pure text generation — fetches nothing' tells the agent that key_pages URLs are not validated or crawled, and the 'no demonstrated effect on Google' caveat honestly characterises the output's utility.

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, front-loaded with what is produced and what it is composed from; every clause (optional convention, no Google effect, fetches nothing) carries usable information. No filler.

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?

For a three-parameter, fully documented, annotated tool with no output schema, the description covers inputs and behavioural character. The only gap is that it never states the return shape (raw text content to be written to a file), which matters slightly since no output schema exists.

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 100%, including nested key_pages with per-field descriptions, so the baseline is 3. The description only restates the three inputs already documented in the schema and doesn't note that key_pages is optional, adding no meaning beyond structured fields.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose4/5

Does the description clearly state what the tool does and how it differs from similar tools?

States a specific verb (Builds) and a concrete artifact (an llms.txt file), plus the exact inputs it composes from. The artifact name alone separates it from generate_robots_txt and generate_schema, and 'fetches nothing' separates it from scan_site, though no sibling is named explicitly.

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?

The note that llms.txt is 'an optional convention ... with no demonstrated effect on Google' is real decision-relevant guidance about whether the tool is worth calling. However, there is no explicit when-to-use/when-not framing relative to alternatives, so the agent must infer the conditions.

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

All Citehound toolsInspect
Try in Browser
Claude Code, inside the Godot 4 editorFlockbay puts an AI agent in the real Godot 4 editor: it writes GDScript, runs your game and fixes what breaks. Free, or bring your own Claude Code or Codex.flockbay.comAd

Glama MCP Gateway

Add one secure layer between your agents and this server.

Resources