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Glama

Medicaid Intelligence

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

A5/5.0
Behavior5/5

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

Beyond the annotations (readOnlyHint, openWorldHint, idempotentHint), the description discloses crucial runtime behavior: parallel fan-out to SEC EDGAR, GDELT→GNews fallback on rate limits/5xx, USPTO PatentsView sunset causing soft-fail, and the exact return shape (changes[] grouped by source + total_changes count + citation URIs). This is exactly the kind of behavioral context agents need. No contradiction 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.

Conciseness5/5

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

The description is dense but every clause earns its place: user-intent examples, data source pipeline, fallback logic, parameter formats, output shape, and an explicit alternative tool. It fronts with user-facing phrasing ('What's new') to maximize quick comprehension, then delivers technical specifics in a tight, structured flow.

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?

This is a high-complexity tool (multi-source fan-out) with no output schema, so the description must carry the full burden of explaining behavior and return values. It does: sources queried, fallback order, failure modes, parameter grammar, and the result structure. The inclusion of pipeworx:// citation URIs also prepares the agent for linking. Nothing critical is missing.

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?

Schema coverage is 100%, and the description enriches the parameters: 'since' accepts ISO or relative shorthand with concrete examples ('7d', '30d', '3m', '1y') and recommends '30d' or '1m' for monitoring; 'value' can be ticker or zero-padded CIK; 'type' is limited to 'company'. This adds practical guidance beyond the schema field descriptions.

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 opens with concrete natural-language examples ('What's new with X', 'latest on Y') and then states a specific verb+resource+scope: 'change feed for a company in the last N days/weeks/months in ONE parallel call.' It also distinguishes itself from entity_profile by naming the alternative, making it clear which tool suits a given intent.

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 provides explicit usage context through exemplar queries and clarifies when NOT to use it: 'Use entity_profile instead when you want the static profile (filings + fundamentals + LEI + patents) regardless of window.' This gives the agent concrete decision rules for tool selection among siblings.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

All Medicaid Intelligence toolsInspect
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TDQS

A3.7/5.0
Disambiguation2/5

Several tools have overlapping purposes: ask_pipeworx, ask_pipeworx_beta, ask_pipeworx_grounded, and deep_research all serve as broad data-query routers. The Polymarket tools (polymarket_edges, polymarket_arbitrage, polymarket_edge_tracker, polymarket_fill_risk, polymarket_kalshi_spread, bet_research) cover closely related trading/arbitrage functions with unclear boundaries. Even the Medicaid drug tools (medicaid_drug_state_market, medicaid_drug_trend, medicaid_drug_utilization) differ only subtly. Agents will struggle to choose correctly.

Naming Consistency3/5

Most tools use snake_case and descriptive phrases, but the patterns are inconsistent: some are verb_noun (generate_llms_txt, list_subscriptions), some are noun_heavy (medicaid_drug_state_market, entity_profile), and some are single verbs (forget, recall, remember). Versioned names like ask_pipeworx_beta and ask_pipeworx_grounded add to the mix. No dominant convention emerges.

Tool Count2/5

39 tools is far too many for a coherent set, especially for a server named 'Medicaid Intelligence.' A large portion of the tools (Polymarket arbitrage, npm dependency scanning, AI visibility checks, pipeworx meta-tools) are unrelated to the server's apparent purpose. The count feels bloated and unfocused.

Completeness3/5

For the Medicaid domain, the coverage is reasonable: drug utilization, enrollment, managed care, and plan market data are present. However, the server also tries to cover general data lookup, prediction markets, and entity research, making the overall surface feel scattered. Missing obvious Medicaid operations (e.g., provider data, claims, spending by state) suggest notable gaps if the stated purpose is Medicaid intelligence.

Resources