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Glama

Pharma Intel

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. Added

TDQS

A4.7/5.0
Behavior5/5

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

Beyond annotations (readOnlyHint, idempotentHint, etc.), description adds fan-out to multiple sources, fallback behavior, parameters (ISO/relative date), and return format (changes grouped by source with citations). No contradiction.

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?

Description is front-loaded with query examples and efficiently organized: examples, mechanics, fallbacks, parameters, return hints, sibling reference. However, it is somewhat verbose; could be tightened without losing clarity.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness4/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given no output schema, description adequately hints at return format (structured changes grouped by source, total_changes count, citation URIs). It also notes soft-fail for USPTO. However, it omits what happens on empty results or error states.

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 descriptions. Description adds value by giving relative shorthand examples ('7d', '3m'), suggesting '30d' or '1m' for typical monitoring, and clarifying 'type' only supports 'company'. Not a 5 because schema already covers basics well.

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 it provides a change feed for a company, given example queries like 'What's new with X' and explicitly distinguishes from sibling 'entity_profile'. Verb+resource+scope are specific and non-tautological.

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?

Explicitly tells when to use (for recent changes) and when not to use (use entity_profile for static profile regardless of window). Also provides fallback logic (GDELT→GNews) and soft-fail for USPTO.

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

A4.1/5.0
Disambiguation5/5

Every tool has a clearly distinct purpose, even within the same domain (e.g., ask_pipeworx vs ask_pipeworx_grounded vs ask_pipeworx_beta are differentiated by groundedness/beta status; polymarket_edges vs polymarket_arbitrage vs polymarket_fill_risk each target discovery vs arbitrage vs execution risk). The descriptions are highly detailed, eliminating ambiguity about when to use each.

Naming Consistency4/5

All tool names use snake_case consistently, and most follow a verb-first pattern (ask_, compare_, discover_, search_, validate_), but a few are noun-first (entity_profile, polymarket_edges, recent_alerts). The style is readable and predictable, though not perfectly uniform in the verb_noun convention.

Tool Count2/5

With 37 tools, the server is heavily over-scoped, especially given the 'Pharma Intel' name that suggests a focused pharma domain. Many tools are general-purpose (prediction markets, memory, subscription management, feedback) unrelated to the server's apparent purpose, making it feel like a grab bag rather than a cohesive set.

Completeness4/5

The pharma-specific tools cover drug profiles, safety, pipeline scans, catalysts, indication landscapes, and sponsor diligence – a solid lifecycle coverage. The broader data/query/prediction-market tools also feel complete for their respective sub-domains. The only minor gaps are niche operations (e.g., updating a subscription), but these are not critical to the core workflows.

Resources