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Glama

Cnpj Br

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

A4.7/5.0
Behavior4/5

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

Annotations already declare readOnlyHint=true, idempotentHint=true, destructiveHint=false. Description adds context on fan-out, fallback logic (GDELT preferred, GNews on rate limit/5xx), and soft-fail for patents. No contradiction.

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 front-loaded with example queries and is well-structured. While slightly verbose, every sentence adds value, conciseness is good given the complexity.

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?

Tool is complex (multiple sources, fallback, no output schema) but description covers inputs, behavior, return structure (grouped changes, total count, citation URIs), and alternative tool. No gaps.

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 is 100% and description adds substantial meaning: explains type=company only, value accepts ticker or CIK, since accepts ISO date or relative shorthand with examples ('7d', '30d', '3m', '1y').

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 over a time window, fanning out to multiple sources (SEC, GDELT/GNews, USPTO). It distinguishes from sibling tool entity_profile which covers static 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 provides example queries ('What's new with X', 'latest on Y') and tells when to use alternative (entity_profile for static profile). Also describes fallback behavior between GDELT and GNews.

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

All Cnpj Br toolsInspect
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TDQS

A3.5/5.0
Disambiguation2/5

Several tools overlap heavily: ask_pipeworx, ask_pipeworx_beta, and ask_pipeworx_grounded are near-identical variants; entity_profile, compare_entities, recent_changes, and deep_research have overlapping research scopes; and the six Polymarket tools cover similar ground. Individual descriptions are detailed, but an agent could easily select the wrong meta-tool.

Naming Consistency3/5

Nearly all names use snake_case, which is readable, but the pattern is inconsistent: some are verb_noun (resolve_entity, list_subscriptions), some are bare noun phrases (entity_profile, polymarket_edges, recent_changes), and memory tools are single verbs (remember, recall, forget). There is no predictable naming convention across the set.

Tool Count2/5

32 tools is already at the heavy end, but the real problem is scope mismatch: a server named 'Cnpj Br' dedicates 31 of 32 tools to Pipeworx data research, prediction markets, memory, and web utilities, with only a single CNPJ lookup. The count feels bloated and incoherent for the apparent purpose.

Completeness1/5

For the stated CNPJ/Brazil domain, the entire surface is one lookup tool: no search by company name, no CNAE/industry breakdowns, no batch or comparative lookups, and no related Brazil KYB data. As a CNPJ-focused server it is severely incomplete.

Resources