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Glama

Ilostat

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

A4.3/5.0
Behavior4/5

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

Beyond annotations (read-only, idempotent), the description discloses that it fans out to SEC EDGAR, GDELT with GNews fallback, and USPTO (soft-fails). It also explains the return structure (changes grouped by source, total_changes, citation URIs). This adds valuable behavioral 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?

The description is a single dense paragraph but contains no fluff. It efficiently conveys the purpose, query pattern, sources, parameter details, and return structure in a compact form. Could be slightly improved with bullet points, but overall concise.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness4/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given the complexity of aggregating multiple sources, the description covers the purpose, parameters, data sources, fallback logic, return structure, and an alternative tool. Without an output schema, it provides a high-level summary of the return format, which is adequate for agent understanding.

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?

The input schema already has 100% coverage with descriptions for all three parameters. The description reinforces this with additional examples (e.g., "7d", "30d" for `since`) and clarifies that `value` can be a ticker or CIK. This adds meaningful usage hints beyond the 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 retrieves a change feed for a company over a specified window, with example queries like "What's new with X" or "latest on Y." It distinguishes itself from sibling tool entity_profile by advising to use that for static profiles instead.

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?

The description explains when to use this tool (for recent changes/delta) and when to use entity_profile (for static profile). It provides guidance on the `since` parameter format (ISO date or relative shorthand) and mentions the tool fans out to multiple sources in one parallel call.

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

A3.9/5.0
Disambiguation2/5

The server mixes three near-identical ask_pipeworx variants (stable, beta, grounded) where beta is currently described as functionally identical to stable, plus several overlapping discovery and research tools (discover_tools, suggest_questions, deep_research, validate_claim, ask_pipeworx). Multiple entity/comparison/change tools (entity_profile, compare_entities, recent_changes) and several Polymarket tools further blur boundaries, requiring careful reading of long descriptions to pick correctly.

Naming Consistency3/5

Many tools follow a clear verb_noun snake_case pattern (list_dataflows, get_data, compare_entities, validate_claim, resolve_entity), and the polymarket_* prefix groups the prediction-market family consistently. However, naming is mixed: bare verbs (remember, forget, recall), noun phrases (dataflow_structure, entity_profile), brand-prefixed tools (pipeworx_feedback, pipeworx_trending), and inconsistent verb choices like ask_pipeworx vs ask_pipeworx_grounded vs suggest_questions.

Tool Count2/5

34 tools is heavy for a server named Ilostat, especially since only three tools (list_dataflows, dataflow_structure, get_data) actually serve ILOSTAT data. The rest form a broad general-purpose data/prediction-market platform that appears bolted on rather than scoped to the server's stated identity.

Completeness4/5

For the ILOSTAT domain specifically, the read-only lifecycle is complete: list_dataflows discovers datasets, dataflow_structure explains dimensions/codes, and get_data retrieves observations — no obvious dead ends for public data access. Other embedded subsystems (memory, subscriptions) also have full CRUD, though the overall server lacks a coherent single-domain surface to judge against.

Resources