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Npi Registry

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 indicate read-only, idempotent, non-destructive. Description adds details: fetches page, extracts title/description/key links, emits single text blob. Contradiction false.

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?

Two sentences plus a bulleted list of use cases, no redundancy. Purpose is 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?

Tool is simple with rich annotations and schema. Description covers purpose, behavior, output, and use cases. No missing information.

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% with descriptions for both parameters. Description does not add extra meaning beyond what schema provides, so baseline 3.

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 it generates an llms.txt file for a URL, explaining extraction and output format. It distinguishes from siblings like 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?

Explicitly lists three use cases: indexing client sites, drafting own project files, auditing competitors. Provides clear context for when to use, but no explicit when-not-to or alternatives.

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
Disambiguation4/5

The tools are generally distinct, with clear purposes for NPI registry operations, but some overlap exists between ask_pipeworx, ask_pipeworx_beta, and ask_pipeworx_grounded, which all route to the same underlying data but with different response modes. Additionally, bet_research and polymarket_edges both offer analysis of prediction markets, causing potential confusion.

Naming Consistency3/5

The naming is mixed: some tools follow a consistent verb_noun pattern (e.g., search, remember, forget), while others use descriptive but non-pattern names like ai_visibility_check or ask_pipeworx_grounded. There is also a mix of snake_case and camelCase (e.g., generate_llms_txt vs. ai_visibility_check).

Tool Count4/5

With 33 tools, the count is slightly high but still reasonable given the broad scope of the server, which covers NPI registry, company profiles, prediction markets, AI visibility, and more. Each tool serves a distinct purpose, though a few could potentially be consolidated.

Completeness3/5

The tool surface covers the NPI registry core (search, get by NPI) but lacks obvious CRUD operations like create, update, or delete for providers. For other domains like company profiles, it has good coverage, but the NPI-specific functionality feels incomplete without lifecycle management.

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