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Glama

Healthdata

by io.github.pipeworx-io

Recent Changes

recent_changes
Read-onlyIdempotent

"What's new with X" / "latest on Y" / "what happened to Z this week / month / quarter" / "updates on Acme" / "news on Tesla recently" / "what's happening with Apple" — change feed for a company in the last N days/weeks/months in ONE parallel call. Fans out to SEC EDGAR (filings since since), GDELT→GNews fallback (news mentions in window — GDELT preferred, GNews when rate-limited or 5xx), USPTO (patents granted; PatentsView API sunset May 2025 so this soft-fails until reactivated). since accepts ISO date ("2026-04-01") or relative shorthand ("7d", "30d", "3m", "1y"). Returns structured changes[] grouped by source + total_changes count + pipeworx:// citation URIs. Use entity_profile instead when you want the static profile (filings + fundamentals + LEI + patents) regardless of window.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
typeYesEntity type. Only "company" supported today.
sinceYesWindow start — ISO date ("2026-04-01") or relative ("7d", "30d", "3m", "1y"). Use "30d" or "1m" for typical monitoring.
valueYesTicker (e.g., "AAPL") or zero-padded CIK (e.g., "0000320193").

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?

Discloses fan-out to multiple sources, fallback behavior, soft-fail for USPTO, and return structure. Annotations already indicate read-only, idempotent, non-destructive; description adds significant context.

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?

Single paragraph with logical flow, but slightly lengthy. Every sentence adds value, but could be trimmed slightly.

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?

Completely explains tool behavior, inputs, outputs, and alternatives, compensating for lack of output schema.

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

Parameters5/5

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

Adds meaning beyond schema: explains since format with examples, recommends default, clarifies value types, and confirms type enum restriction.

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 a change feed for a company across multiple sources, provides example queries, and distinguishes from entity_profile.

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?

Explicit when-to-use guidance with example queries, and when-not-to-use by recommending entity_profile for static profiles.

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

Most tools have clearly described distinct purposes, but a few near-duplicates exist: ask_pipeworx and ask_pipeworx_beta are functionally identical right now, and ai_visibility_check vs scan_competitor_ai_presence overlap. The polymarket sub-family also has multiple edge/fill tools that could be confused.

Naming Consistency3/5

The naming is varied but readable. Many tools follow verb_noun (ask_pipeworx, resolve_entity, search_within, subscribe), yet several are noun phrases (entity_profile, recent_changes, polymarket_edges, bet_research, pipeworx_feedback). There's no single coherent pattern, but the mixture is not chaotic.

Tool Count2/5

At 33 tools, the server is oversized for typical MCP coherence. The breadth is broad (prediction markets, healthcare datasets, memory, subscriptions), but such a large count forces agents to filter through many utilities (suggest_questions, discover_tools, generate_llms_txt) that could be consolidated or hidden.

Completeness4/5

The domain—data querying and analysis—is well covered: universal routing (ask_pipeworx), grounded verification, deep research, entity resolution, dataset metadata, subscriptions, prediction-market edge checks, and memory tools. Minor gaps exist (e.g., no direct health-care-specific analytics batch or file download), but no major dead ends for core workflows.

Resources