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Glama

Data Rennes

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?

Rich behavioral detail beyond annotations: data sources (SEC EDGAR, GDELT→GNews fallback, USPTO with sunset note), grouping of results, return structure including citations. Annotations already confirm read-only and idempotent; description adds non-redundant 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?

Packed with information in a single paragraph. Could be slightly more structured (e.g., bullet points for sources), but not overly verbose. Every sentence earns its place.

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?

Very complete given tool complexity. Covers all data sources, fallback behavior, date parsing, and return structure. No output schema, but description adequately describes return format. Addresses potential confusion points (e.g., PatentsView sunset).

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 100% with descriptions for all parameters. Description adds examples for 'since' (ISO vs relative), clarifies 'value' accepts ticker or CIK, and notes 'type' is restricted to 'company'. Complements schema well.

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 clearly states the tool provides a change feed for a company, covering SEC filings, news, and patents. Uses specific verbs like 'fans out' and gives example queries. Distinguishes 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 tells when to use entity_profile instead for static profile. Provides concrete examples of queries and date formats. No ambiguity about when this tool is appropriate.

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

A4.1/5.0
Disambiguation3/5

Many tools have overlapping purposes, such as multiple data query tools (ask_pipeworx, ask_pipeworx_beta, ask_pipeworx_grounded, deep_research) and several prediction market analysis tools (bet_research, polymarket_arbitrage, polymarket_edges, etc.). While descriptions attempt to differentiate, an agent could easily select the wrong tool.

Naming Consistency3/5

Tool names use a mix of verb-initial (ask_pipeworx, compare_entities) and noun-phrase patterns (entity_profile, dataset_info), with no consistent verb_noun structure. Naming is readable but lacks a predictable pattern.

Tool Count3/5

With 34 tools, the server is on the heavy side. The broad scope (data querying, prediction markets, company research, local open data) somewhat justifies the count, but several tools could be consolidated (e.g., the various polymarket tools).

Completeness4/5

The tool set covers a wide range of functionalities including data querying, company research, prediction market analysis, and memory management. Minor gaps exist (e.g., no user authentication tools beyond subscriptions), but the surface is largely comprehensive for its intended purpose.

Resources