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Glama

Data Nantes

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?

Beyond annotations (readOnlyHint, idempotentHint), description details parallel calls, news fallback logic (GDELT→GNews), USPTO soft-fail due to API sunset, and output structure with grouped changes and citation URIs.

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?

Description is packed with examples and details, but front-loaded with query patterns. Slightly verbose but every sentence serves a purpose. Could be tightened slightly.

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?

No output schema, but description fully explains return structure (changes grouped by source, total_changes, citation URIs) and covers edge cases like fallback behavior and API sunset. Complete for the tool's complexity.

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?

Despite 100% schema coverage, description adds practical usage guidance: explains relative date formats, suggests '30d' for monitoring, clarifies value accepts ticker or CIK, and notes type is limited to 'company'.

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 it provides a change feed for a company, listing recent updates across SEC, news, and patents. It distinguishes from sibling entity_profile which serves static profiles.

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 lists query examples and when to use (e.g., 'What's new with X'), and directs to entity_profile for static profiles. Provides clear context for choosing between tools.

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, especially the Pipeworx query tools (ask_pipeworx, ask_pipeworx_beta, ask_pipeworx_grounded, deep_research) and Polymarket analysis tools (bet_research, polymarket_edges, polymarket_arbitrage). While descriptions are detailed, the similarity in function could confuse an agent trying to select the right one.

Naming Consistency5/5

All tool names follow a consistent snake_case pattern (e.g., ai_visibility_check, ask_pipeworx, bet_research, compare_entities). No mixing of conventions (camelCase, PascalCase) is observed, making it predictable.

Tool Count3/5

With 34 tools, the server is on the heavy side. The majority are Pipeworx tools covering many domains, but the count exceeds the typical sweet spot of 3-15 tools. Some consolidation could reduce redundancy.

Completeness4/5

The tool surface covers a wide range of data access and analysis (Nantes open data, SEC filings, Polymarket, entity comparison, dependency scanning). Minor gaps exist, such as limited Nantes data operations (only query and info) and some tools requiring external accounts, but the overall scope is comprehensive.

Resources