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Glama

Data Paris

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?

Annotations already indicate read-only, open-world, idempotent, and non-destructive behavior. The description adds transparency about multi-source fan-out, fallback mechanisms, soft-fails for USPTO, and the return structure (changes[] grouped by source, 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.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is efficiently front-loaded with query examples and covers all essential aspects without unnecessary words. Every sentence adds value, balancing detail and 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?

For a tool with 3 required parameters, no output schema, and complex multi-source behavior, the description thoroughly covers purpose, parameters, sources, fallbacks, and sibling differentiation. It leaves no significant gaps.

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%, providing a baseline of 3. The description adds value by explaining the 'since' parameter with examples (ISO dates, relative shorthand like '7d', '30d') and typical usage, and gives examples for 'value' (ticker or CIK). It also clarifies that 'type' is currently 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?

The description clearly states the tool provides a 'change feed for a company in the last N days/weeks/months in ONE parallel call' and lists specific data sources (SEC EDGAR, GDELT/GNews, USPTO). It also distinguishes from the sibling tool 'entity_profile', making the purpose unambiguous.

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?

The description includes example queries, specifies that it is for recent changes, and explicitly advises using 'entity_profile' for static profiles regardless of window. It also details fallback behavior between GDELT and GNews, providing clear 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

A4.2/5.0
Disambiguation5/5

Each tool targets a distinct function or data domain, from prediction markets (bet_research, polymarket_*) to company research (entity_profile, compare_entities) to data queries (query, dataset_info). No two tools have overlapping purposes; meta-tools like ask_pipeworx and deep_research are clearly differentiated.

Naming Consistency5/5

All tool names follow a consistent snake_case pattern with descriptive verbs (e.g., search_datasets, validate_claim, remember). No mix of conventions like camelCase or inconsistent verb choices.

Tool Count4/5

At 33 tools, the server covers a broad range of capabilities (data retrieval, AI visibility, prediction markets, company profiles, subscriptions, etc.). While slightly above the ideal range, the count is justified by the breadth of functionality and no tool feels redundant.

Completeness4/5

The tool set provides comprehensive coverage for data discovery, retrieval, and analysis across multiple domains (SEC, FDA, FRED, Paris Open Data, prediction markets, etc.). Minor gaps exist (e.g., no update/delete for Paris Open Data), but the primary focus on reading and analysis is well-served.

Resources