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Ipquery

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

A4.2/5.0
Behavior4/5

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

Annotations already indicate readOnlyHint, idempotentHint, and non-destructive behavior. The description adds behavioral context: 'Fetches the page, extracts title/description/key links, and emits the standard llms.txt markdown format' and specifies output is a 'single text blob'. No contradictions with annotations.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness4/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is front-loaded with the core purpose and includes all essential information. It is slightly longer than necessary but each sentence adds value. Could be trimmed slightly for perfect conciseness.

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 adequately explains the output format ('standard llms.txt markdown format', 'single text blob') and processing steps (fetch, extract, emit). The tool is simple with only two parameters, and the description fully covers what an agent needs to know.

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% for both parameters ('url' and 'max_links'). The description paraphrases the schema (e.g., 'Full URL', 'maximum number of link entries') but does not add significant new semantic meaning beyond what the schema already provides.

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 a production-ready llms.txt file for any URL, using specific verbs and resource. It includes use cases (client indexing, own project, competitor auditing) that help distinguish it from sibling tools like scan_competitor_ai_presence.

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 for when to use the tool (getting a client's site indexed, drafting for own project, auditing competitor), providing clear context. However, it does not explicitly state when not to use it or suggest alternative tools, which prevents a perfect score.

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 are clearly distinct, but the existence of multiple ask_pipeworx variants (standard, beta, grounded) and the overlapping check_risk/lookup_ip tools create meaningful selection ambiguity. Description differentiation helps but doesn't fully resolve it.

Naming Consistency3/5

All names use snake_case, but conventions vary between verb-noun (lookup_ip, compare_entities), noun phrases (entity_profile, recent_alerts), and domain-prefixed names (polymarket_edges, pipeworx_trending). It's readable but not a consistent pattern.

Tool Count2/5

At 33 tools the surface is large, with duplicated ask_pipeworx variants, a modest IP lookup core, and a large number of meta/management tools (memory, subscriptions, onboarding, feedback). Even with a broad scope this feels overloaded, and it is well above the 25+ threshold.

Completeness4/5

The query domain is well covered: entity profiles, comparison, claim validation, deep research, changes, plus memory and subscription management. Minor gaps exist (no direct subscription update, no raw citation fetch utility), but agents can work around them.

Resources