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Glama

Npi Registry

by io.github.pipeworx-io

Pipeworx Trending

pipeworx_trending
Read-onlyIdempotent

What other AI agents are calling on Pipeworx right now. Returns the top tools, top packs, and total call volume over a recent window (24h, 7d, or 30d). Useful for: (1) discovering what data sources are hot for current events, (2) confirming a popular tool is the canonical choice before asking your own question, (3) seeing whether your use case aligns with what most agents need. Self-aggregating signal — derived from CF analytics-engine, no PII, just (pack, tool, count). Cached 5min-1h depending on window.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
windowNo24h (default) | 7d | 30d. Shorter windows surface what's hot right now; longer windows show steady-state demand.

Schema Changelog

Changes observed during successful MCP inspections. Dates show when Glama detected each change.

  1. Added

TDQS

A4.7/5.0
Behavior5/5

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

Annotations already convey readOnly, idempotent, non-destructive. Description adds valuable context: data source (CF analytics-engine), no PII, cached 5min-1h. Provides transparency beyond annotations without contradiction.

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?

Concise three-sentence structure with front-loaded main action. Every sentence adds value: result, use cases, data source/caching. No fluff.

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?

For a simple read-only tool with one optional parameter, the description covers purpose, usage guidance, parameter semantics, and data source/caching. No output schema needed given the simplicity and annotations.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters4/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema coverage is 100% for the single window parameter. Description adds semantic guidance: 'Shorter windows surface what's hot right now; longer windows show steady-state demand.' This enhances understanding beyond the enum values.

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 returns top tools, packs, and call volume over configurable windows. Verb ('returns') and resource ('Pipeworx trending data') are specific. Distinct from sibling tools like ask_pipeworx and deep_research, which have different purposes.

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 concrete use cases (discovering hot sources, confirming canonical choice, seeing alignment). Does not directly state when not to use, but use cases imply boundaries. Caching behavior mentioned adds guidance on data freshness.

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.

Resources