Skip to main content
Glama

Worms

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?

Discloses multi-source fan-out, GDELT→GNews fallback, USPTO soft-fail, return structure (changes[], total_changes, citation URIs), and since parameter behavior. Adds value beyond 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?

Single dense paragraph but efficiently packs information. Could be slightly more structured with bullet points, but every sentence adds value.

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?

Covers all important aspects: parameter formats, source behaviors, return structure, and sibling tool differentiation. No output schema exists, so description adequately explains what the agent receives.

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 coverage is 100%, yet description adds practical guidance: suggests '30d' or '1m' for typical monitoring, clarifies ticker/CIK usage, and confirms only 'company' type supported.

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?

Uses specific verbs like 'change feed for a company' and clearly distinguishes from sibling entity_profile by stating when to use it instead. Examples of natural language queries make it easy to match.

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 provides query examples ('What's new with X'), describes the single parallel call behavior, and directs to entity_profile for static needs. Includes fallback logic and soft-fail conditions.

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

All Worms 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

A3.5/5.0
Disambiguation2/5

Multiple tool clusters blur together: ask_pipeworx, ask_pipeworx_beta, ask_pipeworx_grounded, deep_research, validate_claim, and bet_research all route natural-language questions to sources with overlapping response shapes. The Polymarket family is better differentiated, but an agent faces real selection risk among the query family.

Naming Consistency3/5

All tools use snake_case, which is good, but verb conventions vary widely: ask_/get_/search_/list_/validate_/generate_/scan_ plus noun-only names like ai_visibility_check, entity_profile, bet_research, and the pipeworx_* prefix. The pattern is readable but not predictable enough to guess a tool's function from its name alone.

Tool Count2/5

34 tools is well past the 25+ threshold for a coherent tool set. The server tries to be a universal data gateway, prediction-market toolkit, memory store, subscription manager, WoRMS lookup, and dependency scanner all at once—scope creep that makes the surface unreasonably large.

Completeness2/5

For a data-access server there are notable holes: pipeworx:// citation URIs are advertised as fetchable but no tool resolves them directly; subscriptions can be created, listed, and cancelled but not updated or paused; and the WoRMS component (which matches the 'Worms' server name) has only three lookup tools with no synonym/distribution/export coverage.

Resources