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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. First observed

TDQS

A4.4/5.0
Behavior4/5

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

Annotations already declare readOnlyHint, openWorldHint, idempotentHint, and non-destructive nature. The description adds behavioral steps: HTTP fetch, extraction of metadata, and standard format emission. It does not contradict annotations and usefully enriches the agent's understanding of what happens.

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 dense sentences with no filler. The first sentence states the core action, the second summarizes the workflow, and the third lists explicit use cases. Every sentence adds value, and the structure is front-loaded.

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?

No output schema exists, but the description explains the output is a 'single text blob in standard llms.txt markdown format.' For a simple tool generating a well-known format, this is sufficient. It does not discuss errors, rate limits, or page size limits, but the annotations provide safety context.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters4/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 100%, so baseline is 3. The description provides additional context: 'Full URL of the site to summarize' clarifies the scope, and mention of 'key links' connects to the max_links purpose. It does not add syntax details but gives usage motivation beyond the schema.

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 specific verbs ('generate', 'fetches', 'extracts', 'emits') and clearly identifies the resource ('llms.txt file') and scope ('for any URL'). It distinguishes from siblings by focusing on a unique output format and use case (AI indexing), which is not shared by nearby tools like 'scan_competitor_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 explicitly lists three concrete use cases (client site indexing, personal projects, competitor auditing). While it does not state when to avoid using the tool, the sibling list is large and the tool's purpose is narrow enough that the intended 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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TDQS

B3/5.0
Disambiguation2/5

The tool set mixes four Wikiquote tools with 31 unrelated Pipeworx/Polymarket tools, creating a confusing dual identity. Within the research tooling, ask_pipeworx, ask_pipeworx_beta, and ask_pipeworx_grounded are near-synonyms, and polymarket_arbitrage, polymarket_edges, polymarket_edge_tracker, polymarket_fill_risk overlap heavily, making selection ambiguous.

Naming Consistency3/5

All names are snake_case, but patterns vary: some are verb-first (ask_pipeworx, compare_entities, resolve_entity), some noun-first (entity_profile, polymarket_arbitrage, quote_of_the_day), and several are bare verbs or nouns (search, summary, remember, quotes). Prefix groups like ask_pipeworx* and polymarket_* are consistent, but the overall convention is mixed.

Tool Count2/5

35 tools is excessive for a server named Wikiquote, as only 4 tools (quote_of_the_day, quotes, search, summary) actually serve that domain. The remaining 31 form an unrelated general research and prediction-market toolkit, making the set feel bloated and off-scope for its stated name.

Completeness1/5

As a Wikiquote server, the surface is severely incomplete: there is no random quote, page listing, author/topic browsing, or any write/update operations, and the few quotation tools are buried among unrelated functionality. The unrelated research tools may be internally rich, but they do not address the server's stated purpose.