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Data Dallas

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. First observed

TDQS

A4.5/5.0
Behavior5/5

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

The description discloses the process (fetches page, extracts title/description/key links, outputs markdown) and confirms safety (read-only, non-destructive) aligning with annotations. No contradictions.

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, with a clear main sentence followed by a bullet list of use cases. No unnecessary words, 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 the tool's simplicity (2 params, no output schema), the description covers all needed aspects: functionality, process, output format, and use cases. No gaps.

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 baseline is 3. The description does not add significant detail beyond schema descriptions for url and max_links, offering no extra nuance.

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 tool generates an llms.txt file for any URL, specifying the action and resource. It distinguishes from siblings like ai_visibility_check or scan_competitor_ai_presence by focusing on file generation.

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 lists explicit use cases (indexing client sites, drafting for own project, auditing competitors). While it doesn't mention when not to use or provide direct alternatives, the context is clear and sufficient for selection.

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

A4/5.0
Disambiguation4/5

Most tools have clearly distinct purposes, with a few overlapping pairs (e.g., ask_pipeworx vs ask_pipeworx_grounded, multiple polymarket tools) that could cause mild confusion, but descriptions adequately differentiate them.

Naming Consistency3/5

Tool names consistently use snake_case, but the verb_noun pattern is not consistently applied; some names are noun_noun (dallas_datasets, bet_research) or adjective_noun (ai_visibility_check), creating a mixed nomenclature.

Tool Count2/5

With 33 tools, the server exceeds the typical well-scoped range. While the broad data domain justifies many tools, the count feels heavy and would benefit from consolidation of related functions.

Completeness4/5

The tool set covers a wide array of domains (company data, drugs, economics, prediction markets, memory, subscriptions) with only minor gaps (e.g., no direct web search tool, as ask_pipeworx mostly covers it). Overall, it is comprehensive for its purpose.

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