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Lichess

by io.github.pipeworx-io

Generate llms.txt

generate_llms_txt
Read-onlyIdempotent

Generate a production-ready llms.txt file for any URL so AI crawlers (ChatGPT, Claude, Perplexity) can index the site cleanly. Fetches the page, extracts title/description/key links, and emits the standard llms.txt markdown format. Output is a single text blob ready to drop at site-root/llms.txt. Useful for: getting a client's site indexed by AI, drafting llms.txt for your own project, or auditing how an AI crawler would see a competitor.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
urlYesFull URL of the site to summarize, e.g. "https://example.com" or a specific landing page.
max_linksNoMaximum number of link entries to include (default 25, max 50).

Schema Changelog

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

  1. Added

TDQS

A4.3/5.0
Behavior4/5

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

Beyond annotations (readOnlyHint, idempotentHint), the description explains that it fetches the page, extracts title/description/key links, and emits standard llms.txt markdown. This adds useful behavioral context without contradicting 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?

The description is two clear sentences plus a brief list of use cases. It is front-loaded, efficient, and contains no redundant 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?

For a simple read-only tool with two parameters and no output schema, the description fully explains what the tool does, its output format, and typical use cases. No additional information is needed.

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%, so the description does not need to add much. It reiterates default max_links and max value, which is already in the schema, but provides no additional semantic clarity beyond that.

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 it generates an llms.txt file for a given URL, specifying the action (generate), resource (llms.txt), and output format. It distinguishes this tool from siblings, none of which perform a similar function.

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 explicit use cases (e.g., getting a client's site indexed, drafting for your own project, auditing competitors) and implies when to use it. It does not list when not to use, but the context is clear enough given the sibling tools are unrelated.

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

B3.3/5.0
Disambiguation2/5

The server mixes chess tools with numerous data query tools from Pipeworx, causing significant overlap. Multiple ask_pipeworx variants (ask_pipeworx, ask_pipeworx_beta, ask_pipeworx_grounded) and deep_research have similar purposes, making it difficult for an agent to choose correctly. Chess tools are distinct but compete with many unrelated tools.

Naming Consistency2/5

Tool names follow no consistent pattern: chess tools use mostly underscores (top_players, opening_explorer), Pipeworx tools use mixed styles (ask_pipeworx, deep_research, entity_profile), and memory/subscription tools use simple verbs (remember, subscribe). The naming is inconsistent across the set.

Tool Count2/5

With 41 tools, the count is high and unfocused. A chess server would typically have 10-15 tools; the remaining 31 tools from Pipeworx are unrelated and overwhelm the set. The server tries to cover too many domains, making it bloated for its primary purpose.

Completeness2/5

The chess-specific tools (10) cover basic queries but lack deeper chess analysis (e.g., puzzles, board evaluation). The extensive Pipeworx tools are out of scope for a Lichess server, resulting in an incomplete surface for the expected domain and an excessive surface for unrelated data lookups.

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