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Glama

Security Feeds

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

A4.8/5.0
Behavior5/5

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

Annotations declare the tool read-only, idempotent, and non-destructive. The description adds significant behavioral context: it fans out to multiple APIs, explains fallback logic, soft-fail handling, and describes the return structure (changes[] grouped by source, total_changes count, citation URIs).

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 comprehensive but not overly verbose. It front-loads the purpose with example queries and organizes information logically. Every sentence adds useful information, though it could be slightly trimmed.

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 tool's complexity (multiple data sources, fallbacks, soft-fail), the description covers all necessary aspects. It references the output structure despite no output schema, and explicitly contrasts with the sibling entity_profile 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% with descriptions for all three parameters. The tool description adds value by explaining the since parameter's relative shorthand formats (7d, 30d, 3m, 1y) with a usage suggestion, and provides concrete examples for value (ticker or CIK).

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, listing specific data sources (SEC EDGAR, GDELT/GNews, USPTO) and example queries. It explicitly distinguishes from the sibling tool entity_profile, stating 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 Guidelines5/5

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

The description provides explicit usage context ('What's new with X' queries) and explicitly tells when not to use this tool ('Use entity_profile instead...'). It also explains fallback behavior (GDELT→GNews on rate limit/5xx) and the soft-fail for USPTO.

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

All Security Feeds toolsInspect
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TDQS

A3.9/5.0
Disambiguation2/5

The ask_pipeworx / ask_pipeworx_beta / ask_pipeworx_grounded trio creates real selection ambiguity — beta is currently identical to the stable router, and grounded shares the same routing with an added evidence step. Polymarket tools also overlap (polymarket_edges vs polymarket_arbitrage both surface structural arbitrage), and entity_profile/recent_changes both pull filings and news.

Naming Consistency4/5

Most tools follow a snake_case verb_noun pattern (resolve_entity, list_feeds, validate_claim, compare_entities). Minor deviations exist — bare verbs (remember, recall, forget, subscribe, unsubscribe) and noun-first names (entity_profile, recent_changes, pipeworx_trending) — but the overall convention is consistent and readable.

Tool Count2/5

34 tools is excessive for a server nominally named 'Security Feeds' — only three tools actually relate to security feeds. Even as a broad data-research platform, the surface feels bloated with five near-exclusive Polymarket tools, three memory tools, and four subscription-lifecycle tools that could be consolidated.

Completeness4/5

For the actual domain revealed by the tools (data research, entity intelligence, prediction markets, subscriptions, feeds), coverage is strong: resolution, profiles, changes, comparison, claim verification, memory CRUD, subscription lifecycle, and feed operations are all present. Minor gaps include no subscription-update tool and no direct feed-search tool, but the ask_pipeworx router compensates.

Resources