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Glama

Npi Registry

by io.github.pipeworx-io

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Idempotent

Save data the agent will need to reuse later — across this conversation or across sessions. Use when you discover something worth carrying forward (a resolved ticker, a target address, a user preference, a research subject) so you don't have to look it up again. Stored as a key-value pair scoped by your identifier. Authenticated users get persistent memory; anonymous sessions retain memory for 24 hours. Pair with recall to retrieve later, forget to delete.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
keyYesMemory key (e.g., "subject_property", "target_ticker", "user_preference")
valueYesValue to store (any text — findings, addresses, preferences, notes)

Schema Changelog

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

  1. First observed

TDQS

A4.9/5.0
Behavior5/5

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

Annotations indicate idempotentHint=true and destructiveHint=false. The description adds important behavioral details beyond annotations: scoped by identifier, persistent vs anonymous sessions, 24-hour retention for anonymous. No contradictions.

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 three sentences, each serving a distinct purpose: purpose/usage, when to use, and behavioral details. No redundant or extraneous text. Front-loaded with the core action.

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 the simple tool (2 params, no output schema), the description covers all necessary context: purpose, usage triggers, scope, persistence, and pairing with other tools. No gaps.

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% with parameter descriptions. The description adds value by giving key naming conventions (e.g., 'subject_property') and clarifying value as 'any text'. This provides contextual guidance beyond the raw schema.

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: 'Save data the agent will need to reuse later'. It provides concrete examples of what to store (resolved ticker, target address, preferences) and explicitly distinguishes from siblings 'recall' and 'forget'.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines5/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description gives explicit guidance on when to use: 'Use when you discover something worth carrying forward'. It also tells what not to do indirectly by specifying it's for data the agent needs to reuse. Pairs with recall and forget, providing a complete usage context.

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