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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. Added

TDQS

A4.3/5.0
Behavior4/5

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

Annotations already convey read-only, idempotent, non-destructive behavior. The description adds context: fetches page, extracts specific elements, emits standard format. No contradictions; behavior is well-disclosed.

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 concise: one sentence for purpose, one for process, one for use cases. No redundant information, well-structured and front-loaded.

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?

Given no output schema, the description explains the output format (single text blob) and location (site-root/llms.txt). Annotations cover safety. The tool is simple and complete.

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 adequate descriptions for both parameters (url, max_links). The description does not add meaningful detail beyond the schema, so baseline 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 clearly states the action (Generate), the resource (llms.txt for any URL), and the outcome (markdown file for AI crawlers). It distinguishes from siblings by its specific purpose and mentions extraction of title/description/key links.

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 use cases: indexing a client's site, drafting for own project, auditing competitors. It lacks exclusions or comparisons to siblings but provides clear guidance on when to use.

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

A3.6/5.0
Disambiguation2/5

The set mixes several overlapping families: ask_pipeworx and ask_pipeworx_beta are described as currently identical, while ask_pipeworx_grounded, deep_research, bet_research, and validate_claim all route to the same underlying data sources. Polymarket tools (arbitrage, edges, edge_tracker, fill_risk, spread) also blur together; only the four lookup_* VirusTotal functions are cleanly distinct.

Naming Consistency3/5

Names are uniformly snake_case with a few consistent families (lookup_domain/file/ip/url, ask_pipeworx_*, polymarket_*), which helps. However, the set mixes imperative verbs (remember, forget, subscribe, validate_claim), noun phrases (entity_profile, deep_research, recent_alerts), and inconsistent prefixes, so no single naming convention holds across the server.

Tool Count2/5

35 tools is too many for the apparent purpose, especially for a server named Virustotal. The bulk of the tools address unrelated Pipeworx research, memory, and prediction-market functions, so the count does not reflect the server's advertised domain.

Completeness1/5

For a VirusTotal server, only four lookup tools exist and there is no way to submit a URL/file, create a scan, retrieve analysis details, or explore relationships—core VirusTotal operations are missing. Scoped broadly, the unrelated Pipeworx tools are extensive, but they do not fill the gaps in the advertised domain. The surface is severely incomplete relative to the server name.