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Excuse

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?

The description adds behavioral context beyond the annotations: it fetches the page, extracts specific elements, and emits a single text blob. This includes network behavior and output format, which is helpful. Annotations already cover safety (readOnly, non-destructive), so the added process detail earns a 4.

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, front-loaded with the main purpose, and every sentence adds value. The 'Useful for' list is structured and not redundant. No wasted words.

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, the description explicitly states the output is a single text blob ready for site-root/llms.txt, and covers the process, use cases, and behavioral details. This is complete for the tool's complexity and parameter count.

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 the schema fully documents both parameters. The description itself doesn't add any extra meaning about parameters, but the baseline 3 applies because the schema handles the heavy lifting.

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 purpose with a specific verb ('Generate') and resource ('llms.txt file'), and explains the process (fetches page, extracts title/description/key links, emits standard markdown). This clearly distinguishes it from sibling tools like scan_competitor_ai_presence or ai_visibility_check.

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 'Useful for' section provides explicit contexts: getting a client's site indexed, drafting for own project, auditing competitor AI presence. However, it does not explicitly mention when not to use it or name alternative tools, so it falls 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
Disambiguation2/5

The ask_pipeworx family (ask_pipeworx, ask_pipeworx_beta, ask_pipeworx_grounded, deep_research, validate_claim) all route to the same underlying 5,708-tool catalog with subtle behavioral differences that are easy to misselect. Entity-focused tools (entity_profile, compare_entities, recent_changes, scan_competitor_ai_presence) also overlap heavily in the data they return, making boundaries fuzzy.

Naming Consistency3/5

All names use snake_case, but the word-order convention is mixed: verb-first (ask_pipeworx, compare_entities, discover_tools) coexists with noun-first (entity_profile, polymarket_arbitrage, recent_changes). The inconsistent reversal in excuse_generate versus generate_llms_txt further breaks the predictable pattern.

Tool Count2/5

At 32 tools the set is well past the well-scoped range, and several entries feel like padding: ask_pipeworx_beta duplicates ask_pipeworx, while the seven Polymarket tools and five company-intelligence tools could each be consolidated into fewer distinct capabilities. The broad scope justifies a large surface, but this count makes the server difficult to navigate.

Completeness3/5

The core data-lookup lifecycle (resolve, fetch, ground, validate, research, compare) is well covered, and memory/subscription features round it out. Obvious gaps include no direct tool to read a pipeworx:// citation URI (despite deep_research referencing one), and the excuse_generate tool is an isolated one-off with no supporting tools in its domain.

Resources