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Glama

Semanticscholar

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

A4.7/5.0
Behavior5/5

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

The description discloses thorough behavioral details: fans out to multiple sources, explains failure modes (USPTO soft-fail, GNews fallback), and describes the return format (structured changes grouped by source, total_changes count, citation URIs). Annotations already declare safe, idempotent behavior, and the description adds valuable 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, dense paragraph that packs a lot of information efficiently. It front-loads query examples and key usage notes. While clear, it could be slightly improved with bulleted lists for readability, but it remains concise and well-organized.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness4/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given no output schema, the description adequately explains the return structure (changes grouped by source, total_changes, citation URIs). It covers major behaviors and constraints. Minor gaps exist (e.g., pagination or max results), but overall it is sufficient for an agent to understand the tool's capabilities.

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 is 3. The description adds extra value by explaining the 'since' parameter's date vs relative format, recommending '30d' or '1m' for monitoring, and clarifying that 'value' accepts tickers or CIK numbers. This justifies a score above baseline.

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 companies, fanning out to multiple sources (SEC EDGAR, GDELT/GNews, USPTO). It provides concrete query examples and explicitly distinguishes from the sibling 'entity_profile' tool, making its purpose unmistakable.

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 gives explicit guidance on when to use this tool ('what's new' queries) and when not to (use entity_profile for static profiles). It also explains fallback behavior for news sources and provides usage examples for the 'since' parameter, including a recommendation for typical monitoring.

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

All Semanticscholar toolsInspect
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TDQS

A4/5.0
Disambiguation4/5

Most tools have clearly distinct purposes, especially the core ones like get_paper, search_papers, and entity_profile. However, some pairs like ask_pipeworx and ask_pipeworx_grounded, or the polymarket tools, could cause momentary confusion, though descriptions help differentiate.

Naming Consistency2/5

Naming patterns are inconsistent: tools use verb_noun (e.g., get_paper), noun_phrase (e.g., polymarket_arbitrage), and bare verbs (e.g., forget, recall). There is no unifying pattern, and styles like 'pipeworx_feedback' vs 'search_papers' further add to the inconsistency.

Tool Count3/5

With 34 tools, the set covers a broad range of domains (academic papers, company data, prediction markets, memory, subscriptions). While the scope justifies the number, it feels slightly heavy and could benefit from consolidation or clearer grouping.

Completeness4/5

The tool set covers major functionalities for research, data retrieval, and monitoring, with only minor gaps (e.g., limited to US public companies, npm-only dependency scanning). Overall, the surface is comprehensive for the stated capabilities.

Resources