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Glama

Science 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?

Beyond annotations (readOnlyHint, idempotentHint), the description details the tool's behavior: fans out to SEC EDGAR, GDELT→GNews fallback, USPTO soft-fail, and returns structured changes with citation URIs. This adds significant 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?

The description is a single, front-loaded paragraph with no wasted sentences. While it is dense, it efficiently conveys all needed information for a complex tool.

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 multi-source complexity, lack of output schema, and many siblings, the description covers all essential aspects: sources, fallbacks, return structure, and differentiation from siblings. No gaps.

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%, but the description adds value by explaining 'since' with examples (ISO date and relative shorthand) and clarifying that 'type' only supports 'company'. This goes beyond the schema descriptions.

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 specifies the tool's purpose with clear verb+resource: 'change feed for a company in the last N days/weeks/months'. It provides example queries and distinguishes from 'entity_profile', a sibling tool for static profiles.

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 explicitly states when to use this tool vs. alternatives: 'Use entity_profile instead when you want the static profile... regardless of window.' It also explains fallback behavior and limitations, providing clear guidance.

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

All Science Feeds toolsInspect
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TDQS

A3.7/5.0
Disambiguation2/5

Several tools have overlapping purposes: ask_pipeworx, ask_pipeworx_beta (currently identical), ask_pipeworx_grounded, deep_research, and validate_claim all route questions to data, while the six polymarket_* tools blur edge-finding, arbitrage, and fill-risk. ai_visibility_check and scan_competitor_ai_presence also overlap, with the latter wrapping the former.

Naming Consistency3/5

All names are snake_case and readable, with clear families like polymarket_*, ask_pipeworx*, and list_*. However, conventions mix verb-first names (resolve_entity, read_feed) with noun-first names (entity_profile, pipeworx_trending, recent_alerts, deep_research), so no single pattern dominates.

Tool Count2/5

34 tools is heavy for any server, but the real issue is scope sprawl: science feeds, a universal data router, prediction-market analysis, memory, subscriptions, AI-visibility checks, and npm scanning each form a mini-server. The count is not defensible for the 'Science Feeds' purpose and would be better split into several focused servers.

Completeness2/5

There is no coherent domain to assess completeness against—'Science Feeds' describes only 3 of 34 tools. Individual clusters are partially complete (subscriptions have subscribe/list/unsubscribe/recent_alerts, memory has remember/recall/forget), but the overall surface is a grab-bag of features from unrelated products, with obvious gaps in each (e.g., no way to update a subscription, no direct access to specific data packs except through the router).

Resources