Skip to main content
Glama

Kegg

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?

Describes calls to SEC EDGAR, GDELT/GNews, USPTO, soft-failure for patents, and return format (changes[], total_changes, citation URIs). Annotations already indicate safety; description adds rich 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.

Conciseness5/5

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

Dense but well-structured paragraph with example queries, technical details, and clear separation of concerns. Every sentence provides unique 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?

No output schema, but description explains return structure (changes[], total_changes, citation URIs). Covers all parameters, fallbacks, and limitations (patents soft-fail). Complete for an information retrieval tool.

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%, so baseline 3. Description adds value by explaining `since` accepts ISO or relative with examples, `value` as ticker/CIK, and typical monitoring window suggestion.

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 defines the tool as a change feed for a company over a recent window, with example queries. It distinguishes from the sibling `entity_profile` by explicitly contrasting use cases.

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?

Provides explicit guidance on when to use (change feed queries) and when to use `entity_profile` instead. Also explains fallback behavior between GDELT and GNews.

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

All Kegg 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

A3.6/5.0
Disambiguation2/5

The three ask_pipeworx variants heavily overlap, with ask_pipeworx_beta currently matching ask_pipeworx exactly, and the six polymarket_* tools have blurry boundaries between scanning, arbitrage, and fill-risk analysis. The many non-KEGG research tools also make it easy to confuse unrelated purposes with the server's nominal bioinformatics focus.

Naming Consistency3/5

All names use snake_case, but the conventions vary widely: single verbs (find, remember, subscribe), verb_noun pairs (get_entry, resolve_entity), noun phrases (entity_profile, bet_research), and vendor prefixes (pipeworx_*, polymarket_*). It is readable but not a consistent, predictable pattern.

Tool Count1/5

34 tools is far beyond the well-scoped range, and the server is named 'Kegg' while only 3 of the 34 tools actually relate to KEGG bioinformatics. The rest belong to an unrelated Pipeworx data-research and prediction-market platform, an extreme mismatch between name, purpose, and tool count.

Completeness2/5

For a KEGG server, the surface is severely incomplete: only find, get_entry, and list_database exist, with no batch retrieval, cross-database queries, or pathway-organism mapping. The broader Pipeworx toolset is rich for data research but entirely disconnected from the server's stated purpose, leaving the actual domain under-covered.

Resources