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Lemmy

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?

The description discloses the tool's behavior beyond annotations: it 'Fetches the page, extracts title/description/key links, and emits the standard llms.txt markdown format.' It also describes the output as a 'single text blob ready to drop at site-root/llms.txt.' Annotations already cover readOnly and idempotent hints, so this adds useful process detail, though it lacks edge-case information like failure handling.

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 three sentences that front-load the purpose, then explain the process and output, followed by a compact use-case list. Every sentence earns its place with no fluff, making it highly concise and well-structured.

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 tool with only two parameters and no output schema, the description is complete: it explains the output format ('a single text blob'), the use cases, and the process. An agent has enough context to select and invoke it correctly without needing additional details.

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% with both 'url' and 'max_links' already well-described in the input schema. The description adds minimal extra meaning (e.g., 'key links' hints at max_links' purpose), but the schema carries the heavy lifting. This aligns with the baseline 3 for high schema coverage.

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 a specific verb and resource: 'Generate a production-ready llms.txt file for any URL.' It explains the output format and the extraction process, distinguishing itself from siblings like ai_visibility_check or scan_competitor_ai_presence by focusing on producing the actual llms.txt file rather than just checking or auditing AI presence.

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 'Useful for' list with three concrete scenarios (client indexing, own project drafting, competitor auditing), offering clear context on when to use. However, it does not explicitly state when not to use or name alternative tools, so it stops short of full alternative/exclusion 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

B3.4/5.0
Disambiguation2/5

Many tools have overlapping purposes: ask_pipeworx, ask_pipeworx_beta, ask_pipeworx_grounded, deep_research, and validate_claim all answer factual questions, while bet_research, polymarket_edges, polymarket_arbitrage, and polymarket_fill_risk all analyze prediction markets. The Lemmy tools are distinct from the Pipeworx tools, but within each cluster an agent could easily select the wrong tool despite lengthy descriptions.

Naming Consistency2/5

Naming conventions are mixed: single nouns (post, community, site), verb_noun patterns (list_subscriptions, resolve_entity, generate_llms_txt), and ad-hoc names (ask_pipeworx, forget, recall). There is no consistent verb_noun or noun-only pattern across the set, making it hard to predict tool names.

Tool Count2/5

38 tools is excessive for a server named 'Lemmy' where only 7 tools (comments, communities, community, post, posts, search, site) actually relate to Lemmy. The vast majority of tools belong to an unrelated Pipeworx data platform, creating a severe scope mismatch and making the server feel bloated and unfocused.

Completeness2/5

For the Lemmy domain, the tools cover read-only browsing (list posts, view post, list communities, fetch community, comments, search, site metadata) but completely lack write operations like posting, commenting, voting, or moderation. The Pipeworx tools are extensive but do not compensate for the primary domain's gaps since the server is ostensibly about Lemmy.

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