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Glama

Insee

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

A4.5/5.0
Behavior4/5

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

Annotations already declare readOnlyHint, idempotentHint, and openWorldHint. Description adds valuable behavioral traits: parallel fan-out to SEC EDGAR, GDELT→GNews fallback, USPTO soft-fail due to API sunset, and return format. 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?

Description is comprehensive yet front-loaded with core purpose and examples. Every sentence adds value—covers data sources, fallbacks, parameter hints, and sibling differentiation. No redundancy.

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 the tool's complexity (multi-source, fallbacks, soft-fail, relative dates) and no output schema, the description fully prepares the agent. Provides return structure, citation URIs, and links to sibling tool. Missing nothing essential.

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 description coverage is 100%. Description enhances semantics by explaining 'since' format with examples (ISO date and relative shorthand like '30d'), and clarifying 'value' can be ticker or CIK. Adds context beyond schema.

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' with specific verb ('change feed') and resource ('company'). It distinguishes from sibling 'entity_profile' by contrasting dynamic vs static data.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines4/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

Explicitly lists example use cases ('what's new with X', 'latest on Y') and directs to alternative 'entity_profile' for static profiles. Does not exhaustively describe when not to use, but the context is clear.

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

All Insee toolsInspect
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TDQS

A3.9/5.0
Disambiguation3/5

Most tools have distinct purposes, but the ask_pipeworx trio (ask_pipeworx, ask_pipeworx_beta, ask_pipeworx_grounded) creates genuine confusion — the beta is explicitly identical to the stable router right now. The Polymarket cluster (bet_research, polymarket_edges, polymarket_arbitrage, polymarket_kalshi_spread) also overlaps in the 'find a betting edge' space, though the descriptions do a decent job of carving out niches.

Naming Consistency3/5

Names follow a readable snake_case style with useful prefixes (ask_pipeworx, pipeworx_, polymarket_), but the set mixes verb-first (compare_entities, resolve_entity), noun-first (entity_profile, recent_alerts), and bare verbs (remember, recall, forget) with no consistent convention. The per-domain prefixes provide some predictability, but the overall pattern is not uniform.

Tool Count2/5

32 tools is above the 25+ threshold for 'too many,' and the set feels bloated with hyper-niche utilities (generate_llms_txt, scan_dependency, polymarket_edge_tracker) that are unrelated to the server's apparent INSEE identity. Even as a broad data platform, several tools could be consolidated (the three ask_pipeworx variants, the AI-visibility single/comparison pair).

Completeness3/5

The Pipeworx core workflows are well-covered: routing, grounded answers, deep research, entity profiles, comparisons, claim verification, memory, subscriptions, and alerts all close loops. However, the server is named 'Insee' but only one of 32 tools touches French business registry data, leaving the implied domain almost entirely absent; peripheral one-off tools (npm, llms.txt) also have no supporting lifecycle tools.

Resources