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Glama

Uniprot

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

A4.6/5.0
Behavior5/5

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

Beyond annotations (readOnly, idempotent, etc.), the description details fan-out to multiple sources, fallback logic from GDELT to GNews, and soft-fail for USPTO. It also mentions return structure with citation URIs, adding significant behavioral 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?

Front-loaded with examples, then explains sources, parameters, and sibling differentiation. Every sentence adds value, though it could be slightly more concise. Well-organized overall.

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?

With no output schema, the description adequately covers return structure (changes grouped by source, total_changes count, citations) and failure modes (USPTO soft-fail). Given the tool's multi-source complexity, this is complete and actionable.

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 has 100% description coverage, but description adds extra context: examples for 'since', explanation that 'type' only supports 'company', and that 'value' accepts ticker or CIK. This adds useful nuance beyond the 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 provides a change feed for a company in a recent time window, combining SEC filings, news, and patents. It distinguishes itself from the sibling entity_profile by explicitly saying when to use that instead.

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

Usage Guidelines4/5

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

Provides concrete usage examples and parameter guidance (e.g., 'since' accepts ISO or relative). Explicitly contrasts with entity_profile. Could improve by stating situations where this tool should not be used, but the differentiation is clear.

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

A3.6/5.0
Disambiguation2/5

Several tools have overlapping purposes: ask_pipeworx, ask_pipeworx_beta, and ask_pipeworx_grounded all route to the same 5,440 tools, with the beta currently identical to the stable version. Additionally, discover_tools and suggest_questions both serve as capability discovery entry points, and the five polymarket_* tools share similar prefix and some functional overlap. Generic names like get and search further blur boundaries, especially when 'get' could be mistaken for a generic fetch rather than a UniProt accession lookup.

Naming Consistency2/5

Tool names mix single-word verbs (get, search, keyword), noun compounds (feature_summary, entity_profile), and prefixed families (ask_pipeworx_*, polymarket_*, scan_*). While most names use snake_case, the verb-noun pattern is inconsistent: some are action-first (ask_pipeworx, resolve_entity) and others are object-first (proteomes_search, taxonomy_search). There is no uniform convention, making it hard to predict tool names.

Tool Count1/5

The server is named 'Uniprot' but only 7 of 37 tools actually relate to UniProt protein data; the rest are a broad Pipeworx data platform covering prediction markets, AI visibility, memory, subscriptions, and more. This is an extreme mismatch between the stated product and the tool surface, far exceeding the expected scope for a protein database server.

Completeness2/5

For the apparent UniProt purpose, the surface has significant gaps: no batch retrieval, no ID mapping from gene names or other databases, no sequence alignment or BLAST, and no access to UniRef/UniParc. The Pipeworx tools, while extensive via the ask_pipeworx router, still lack dedicated tools for many advertised data categories and are not comprehensive for a standalone data platform. The overall surface feels incomplete for any single coherent domain.

Resources