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Yesterdays Number

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?

Annotations already provide readOnlyHint, openWorldHint, idempotentHint, destructiveHint. Description adds behavioral details: 'Fetches the page, extracts title/description/key links, and emits the standard llms.txt markdown format.' This goes beyond annotations by explaining the internal process and output format.

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?

Description is two sentences plus a concise list of use cases. It is front-loaded with the primary purpose and efficiently conveys all necessary information without redundancy.

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?

Despite no output schema, description fully explains the output (single text blob, markdown format, ready for site-root). It covers purpose, behavior, parameters, and use cases. Rich annotations compensate for any missing 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 covers 100% of parameters with clear descriptions. The description adds no additional meaning 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?

Description clearly states the tool generates a production-ready llms.txt file for any URL, specifying the verb ('generate'), resource ('llms.txt file'), and context ('for any URL, so AI crawlers can index the site cleanly'). It lists concrete use cases (client's site, own project, competitor audit), which distinguishes it from sibling tools like scan_competitor_ai_presence that have a different focus.

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?

Description explicitly states when to use the tool via a bulleted list of useful scenarios. While it does not explicitly state when not to use or name alternatives, the provided context is sufficient for typical use cases.

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

A3.9/5.0
Disambiguation3/5

Many tools have overlapping purposes, such as multiple query tools (ask_pipeworx, ask_pipeworx_grounded, deep_research) and prediction market analysis tools (bet_research, polymarket_arbitrage, polymarket_edges). While descriptions help differentiate, the clustering of similar functions may cause confusion.

Naming Consistency4/5

All tools use snake_case, but the pattern varies: some start with verbs (forget, remember, recall) while others start with nouns (entity_profile, pipeworx_trending). The naming is generally clear with minor inconsistencies.

Tool Count4/5

With 31 tools, the set is slightly large but appropriate given the broad scope covering company data, prediction markets, memory, subscriptions, and more. A few tools like yesterdays_number_get seem out of place, but overall the count is reasonable.

Completeness3/5

The tool set covers many domains comprehensively (SEC, FDA, FRED, prediction markets), but there are notable gaps such as no direct web search or stock price tool beyond routed queries. The server feels like a collection of capabilities rather than a unified domain.

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