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Glama

Gwas Catalog

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?

The description adds significant context beyond annotations, including fan-out to multiple sources, fallback logic, soft-fail for USPTO, and return structure (changes[] with URIs). No contradictions 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.

Conciseness4/5

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

The description is well-structured and front-loaded but slightly lengthy due to detailed explanations of sources and fallback. It could be more concise while retaining key information.

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 complexity (multiple sources, fallback, soft-fail, three required parameters) and no output schema, the description covers all aspects: purpose, parameters, sources, fallback, return type, and alternatives without gaps.

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?

The description adds practical guidance beyond the schema: type is limited to 'company', value accepts ticker or CIK, and since includes relative date shorthand with recommended typical values. Schema coverage is 100%.

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 provides a change feed for a company in a recent time window, specifying sources (SEC, GDELT, USPTO) and distinguishing from the sibling entity_profile tool.

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 example queries and explicitly recommends an alternative (entity_profile) when a static profile is needed. It also explains fallback behavior (GDELT to GNews) and soft-fails 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

A3.7/5.0
Disambiguation2/5

The set has distinct clusters, but several tools are near-doppelgangers: ask_pipeworx_beta is explicitly identical to ask_pipeworx, ask_pipeworx_grounded differs only in evidence handling, and deep_research/validate_claim overlap with the same routing pipeline. Prediction-market and company-research tools also blur together despite detailed descriptions.

Naming Consistency4/5

Overall naming is mostly consistent verb-first snake_case (get_snp, resolve_entity, list_subscriptions, compare_entities). It misses a 5 because of noun-led entries (entity_profile, snp_associations) and brand-prefixed outliers (pipeworx_feedback, pipeworx_trending, ask_pipeworx variants).

Tool Count2/5

34 tools is well into the too-many range and the count is not justified by the server's stated GWAS Catalog purpose: only get_snp, snp_associations, and studies_by_trait actually serve that domain, while the rest are a broad Pipeworx/prediction-market/memory/subscription grab bag.

Completeness2/5

For a GWAS Catalog server, only three domain-specific lookups exist: SNP lookup, SNP-to-association, and trait-to-studies. There are obvious gaps such as study-by-accession, association tables per study, trait search, or region/gene-based queries. The generic ask_pipeworx router may paper over this, but the named domain's surface is thin and the unrelated tools don't help.

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