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Glama

Edgar

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. Added

TDQS

A4.9/5.0
Behavior5/5

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

Annotations already declare readOnlyHint, idempotentHint, and no destruction. The description adds significant detail: multi-source fan-out, fallback behavior, soft-fail for USPTO, and the structure of the return value (grouped changes, total_changes, citation URIs). No contradictions.

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 relatively long but well-structured with example queries upfront, then details. Every sentence adds value, though it could be slightly tighter. Still earns a 4 for efficient communication.

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 three required parameters and no output schema, the description covers return structure, source behaviors, fallbacks, and error modes (soft-fail). The description is thorough, making the tool well-understood without additional output docs.

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%, but description adds substantial value: explains date formats for 'since' with examples, recommends '30d' for monitoring, clarifies 'value' accepts ticker or CIK, and notes 'type' is limited to 'company'. This goes well 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 uses specific verbs like 'returns' and 'feed', names the resource 'change feed for a company', and explicitly contrasts with sibling tool 'entity_profile' for static profile. It clearly defines the scope (last N days/weeks/months) and sources.

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?

Provides explicit when-to-use guidance with example queries and directs to 'entity_profile' for static profiles. The mention of fallback logic (GDELT→GNews) also helps in understanding usage context.

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

A4.3/5.0
Disambiguation5/5

Each tool has a clearly distinct purpose, with detailed descriptions that prevent confusion. Overlapping tools like ask_pipeworx vs ask_pipeworx_grounded are explicitly differentiated by use case (casual vs high-stakes) and refusal behavior.

Naming Consistency4/5

Names are mostly consistent using lowercase underscores, but there is a mix of verb-initial (ask_pipeworx, generate_llms_txt) and noun-initial (edgar_company_facts, polymarket_arbitrage) patterns, which slightly reduces predictability.

Tool Count3/5

37 tools is high, but the server covers a broad domain of authoritative data sources (SEC, FDA, FRED, prediction markets, etc.). While it exceeds the typical 15-tool threshold, each tool serves a distinct data need and the count feels justified for the scope.

Completeness5/5

The tool surface covers the full lifecycle of data retrieval and analysis: discovery (discover_tools), single queries (ask_pipeworx), grounded lookups (ask_pipeworx_grounded), multi-source research (deep_research), entity profiles, comparisons, historical data, and subscription monitoring. No major gaps are evident.

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