Skip to main content
Glama

Datacite

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?

Annotations already mark it as read-only, open-world, idempotent, and non-destructive. Description adds fan-out behavior, source fallbacks, and soft-fail for USPTO patents.

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 is information-dense but well-organized with examples. Could be slightly more structured, but still concise for the amount of detail.

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?

Despite no output schema, description explains return structure (changes grouped by source, total_changes count, citation URIs). Covers all key aspects for a 3-param tool.

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 has 100% coverage. Description adds contextual examples for `since` (ISO/relative), explains `value` accepts ticker or CIK, and notes `type` is limited to 'company'.

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 provides a change feed for a company, listing SEC filings, news, and patents. Includes concrete query examples and distinguishes from sibling '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?

Explicitly tells when to use this tool vs. 'entity_profile', explains fallback from GDELT to GNews, and provides guidance on `since` parameter format.

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

All Datacite toolsInspect
Try in Browser
RayrunBuild, deploy, and operate MCP servers without becoming an infrastructure team.ray.runAd

Glama MCP Gateway

Add one secure layer between your agents and this server.

TDQS

A3.7/5.0
Disambiguation2/5

Several tools occupy overlapping roles: ask_pipeworx, ask_pipeworx_beta, and ask_pipeworx_grounded are near-identical entry points (beta currently behaves exactly like stable), while entity_profile, recent_changes, and compare_entities all target company research. The prediction-market cluster (bet_research, polymarket_edges, polymarket_arbitrage, polymarket_edge_tracker, polymarket_fill_risk, polymarket_kalshi_spread) is large enough that an agent could easily select the wrong one.

Naming Consistency2/5

Naming conventions are mixed throughout: verb_noun patterns (search_dois, list_repositories, validate_claim) coexist with noun phrases (entity_profile, recent_alerts, ai_visibility_check) and bare verbs (remember, recall, forget, subscribe). The only consistent thread is snake_case, but the grammatical style is unpredictable.

Tool Count2/5

At 34 tools, the server is overstuffed, especially given its apparent focus is DataCite but only 3 tools actually serve that domain (get_doi, search_dois, list_repositories). The rest are a grab bag of Pipeworx data routing, prediction-market analytics, memory, and subscription utilities that would be better split into separate focused servers.

Completeness2/5

The DataCite surface is incomplete (no DOI creation, update, or deletion despite DataCite supporting registration), while the Pipeworx surface has no direct fetch-by-URI tool for the pipeworx:// citations that other tools return. The presence of a duplicate beta router and a beta router with no active differences further muddies the coverage picture.

Resources