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Glama

Quiver

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?

Annotations already indicate read-only, idempotent, non-destructive. Description adds significant behavioral details: fans out to three sources in parallel, fallback logic (GDELT→GNews), rate-limit handling, soft-fail for USPTO, and return format. No contradiction.

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?

Single paragraph of ~150 words, front-loaded with purpose and examples. Every sentence provides necessary information without redundancy. Efficiently covers multiple sources and edge cases.

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?

Despite no output schema, the description specifies return structure (changes[] grouped by source, total_changes count, pipeworx:// URIs). Covers all three sources, window options, fallback, and distinguishes from entity_profile. Complete for a complex 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 good descriptions. The description adds value by explaining shorthand for 'since' and showing example usage ('7d', '30d'), clarifying that 'value' can be ticker or CIK, and noting 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?

The description clearly states the tool's purpose: change feed for a company in the last N days/weeks/months, with specific verb 'get changes' and resources (SEC EDGAR, GDELT→GNews, USPTO). It distinguishes from sibling 'entity_profile' by advising when to use the alternative.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines4/5

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

Provides explicit usage context with example queries ('What's new with X') and explicitly recommends entity_profile for static profiles. Does not cover all sibling alternatives but gives clear fallback behavior for GDELT→GNews.

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

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TDQS

A4.2/5.0
Disambiguation5/5

Each tool has a clearly distinct purpose with detailed descriptions. Overlapping areas like polymarket tools are well-differentiated by function (research, arbitrage, edge tracking, fill risk, cross-venue spread). The meta-tools (discover_tools, suggest_questions) further reduce ambiguity.

Naming Consistency5/5

All tool names use a consistent snake_case pattern (e.g., ask_pipeworx, bet_research, polymarket_edges). The naming is descriptive and predictable, aiding agent selection.

Tool Count3/5

34 tools is on the high side for typical MCP servers. While many are justified by the platform's breadth, the count slightly exceeds the ideal range, potentially overwhelming agents.

Completeness4/5

The tool set covers a wide array of domains (company data, drugs, prediction markets, economics, news, etc.) with CRUD-like operations for subscriptions and memory. Minor gaps exist (e.g., no direct social media or custom API tool), but overall the surface is well-matched to the server's data platform purpose.

Resources