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Speedrun

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).

TDQS

A4.3/5.0
Behavior4/5

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

The description discloses key behaviors beyond the annotations: it fetches the page, extracts title/description/key links, and outputs a single text blob in standard llms.txt markdown format. Annotations already declare readOnlyHint=true and destructiveHint=false, so the added process details meaningfully enrich understanding without redundancy.

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, front-loaded with the core purpose and process, and every sentence adds value. The 'Useful for' list is concise and practical, with no fluff or repetition.

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?

The tool is simple (2 params, no output schema), and the description covers purpose, process, output format, and use cases. It explicitly states the output is 'a single text blob ready to drop at site-root/llms.txt', compensating for the lack of an output schema. Annotations cover safety and idempotency, making the description 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 description coverage is 100%, so both parameters (url, max_links) are already well-documented. The description indirectly mentions 'key links' which relates to max_links, but adds no additional semantic detail beyond the schema. 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 tool's function: 'Generate a production-ready llms.txt file for any URL' with a specific verb, resource, and scope. It also distinguishes from siblings by focusing on llms.txt generation, which is unique among the listed tools.

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' section listing three concrete scenarios (client indexing, own project drafting, competitor auditing), giving clear context for when to use the tool. However, it does not explicitly mention alternatives or when not to use it, so it stops short of a 5.

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

A3.8/5.0
Disambiguation3/5

Many tools have distinct purposes, but there is overlap in data querying tools (ask_pipeworx, ask_pipeworx_grounded, deep_research, entity_profile, etc.) and prediction market tools (bet_research, polymarket_arbitrage, polymarket_edges, etc.). Detailed descriptions help differentiate them, but the number of similar-sounding tools increases the chance of misselection.

Naming Consistency3/5

Tool names show mixed conventions: some use verb_noun (e.g., find_user, list_subscriptions), others are noun_verb (e.g., entity_profile, bet_research), and there are prefixes like polymarket_ and pipeworx_. While subgroups are internally consistent, the overall set lacks a unified pattern.

Tool Count2/5

With 34 tools spanning speedrun.com queries, Pipeworx data access, memory management, subscriptions, and prediction markets, the server bundles multiple domains. The scope is too broad for a coherent single server; splitting into separate servers would improve usability.

Completeness4/5

Within each domain (speedrun.com, Pipeworx, Polymarket), the tool set covers key operations comprehensively, including research, arbitrage, fill risk, and monitoring. Minor gaps exist (e.g., no tool to place bets), but the overall surface is well-covered for the advertised functionalities.

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