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Glama

Eodhd

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

A4.9/5.0
Behavior5/5

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

Describes fan-out to SEC EDGAR, GDELT→GNews fallback, USPTO soft-fail, return structure with grouped changes and citations, adding substantial context beyond annotations.

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?

Well-structured with examples and clear sections, but slightly verbose; every sentence earns its place, though a bit of trimming could improve conciseness.

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?

Comprehensive for a multi-source tool: outlines sources, fallbacks, return hints, and directs to alternative tool for static data; lacks only minor error details which are not critical.

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?

All parameters are described in schema (100% coverage), and description adds useful details like accepted date formats, relative shorthand, ticker/CIK input, and enum restriction.

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?

Description starts with concrete example queries and clearly states the tool provides a change feed for a company from multiple sources (SEC, news, patents) in one parallel call, distinguishing it from sibling `entity_profile`.

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?

Explicitly advises to use `entity_profile` for static profiles instead of change feed, and provides example query patterns and typical monitoring recommendations.

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

All Eodhd toolsInspect
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TDQS

A3.5/5.0
Disambiguation2/5

Multiple tool clusters overlap heavily: ask_pipeworx, ask_pipeworx_beta, ask_pipeworx_grounded, and deep_research all route research questions; polymarket_arbitrage, polymarket_edges, polymarket_edge_tracker, and bet_research all hunt prediction-market edges; ai_visibility_check and scan_competitor_ai_presence do nearly the same job. The verbose descriptions help, but an agent would still struggle to pick the right tool in these clusters.

Naming Consistency2/5

Everything is snake_case, but there is no coherent pattern: some names are verb_noun (compare_entities, validate_claim), some are bare verbs (remember, forget, recall), some are prefixed by domain (eodhd_*, pipeworx_*, polymarket_*, ai_visibility_*), and the Eodhd prefix clashes with the Pipeworx family. The conventions feel inherited from multiple unrelated codebases rather than a unified design.

Tool Count2/5

33 tools is already heavy for most servers, but the real problem is that only 2 tools (eodhd_eod_prices, eodhd_fundamentals) relate to the server's stated purpose. The other 31 span Pipeworx research, Polymarket betting, memory, subscriptions, feedback, llms.txt generation, and npm dependency checking — a massively over-scoped grab bag for a server named 'Eodhd'.

Completeness2/5

For the nominal EODHD domain the surface is severely incomplete: no splits, dividends, options, exchanges list, or other standard EODHD endpoints — just prices and fundamentals. As a general research/data platform it is broad rather than deep, mixing a decent question-answering core with unrelated one-off utilities, so no single coherent lifecycle is fully covered.

Resources