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Glama

Dados Pt

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

A4.6/5.0
Behavior5/5

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

Annotations declare readOnlyHint=true, idempotentHint=true, destructiveHint=false. The description adds substantial behavioral context: fans out to multiple sources (SEC, GDELT, USPTO), fallback from GDELT to GNews, soft-fail for patents, structured return format, and citation URIs. This goes well beyond what annotations provide.

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 relatively long but front-loaded with example queries. Each sentence adds value (sources, fallback, parameter format, alternative tool). It could be slightly trimmed without losing information, but it remains efficient.

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 three required parameters and no output schema, the description explains the return structure (changes[] grouped by source, total_changes count, pipeworx:// citation URIs). It covers source fallbacks, parameter options, and sibling differentiation. The tool's complexity (multiple data sources, failure modes) is well documented.

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% with parameter descriptions. The description adds extra clarification: explains `since` accepts ISO date or relative shorthand with examples, recommends '30d' or '1m', and notes `type` only supports 'company'. This adds meaningful guidance 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 starts with example queries like "What's new with X" and explicitly states it provides a 'change feed for a company in the last N days/weeks/months in ONE parallel call.' It distinguishes from sibling tool `entity_profile` which is for 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 Guidelines4/5

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

The description clearly states when to use this tool vs `entity_profile`: 'Use entity_profile instead when you want the static profile (filings + fundamentals + LEI + patents) regardless of window.' It does not provide explicit when-not-to-use scenarios, but the alternative is well-defined.

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

A3.8/5.0
Disambiguation3/5

Most tools are cleanly separated, but real overlap exists: ask_pipeworx_beta is currently an identical twin of ask_pipeworx, ask_pipeworx_grounded shares the same router, ai_visibility_check is subsumed by scan_competitor_ai_presence, and several polymarket tools scan adjacent opportunity spaces. The long descriptions help an agent choose, but the boundaries between several query, research, and edge-scanning tools are not always crisp.

Naming Consistency3/5

Names are uniformly lowercase and mostly snake_case, but they mix verb_noun forms like search_datasets and validate_claim with noun-first/brand-prefixed forms like entity_profile, pipeworx_trending, polymarket_arbitrage, and recent_alerts, plus bare verbs like remember and forget. There is no single predictable pattern, though the names remain readable.

Tool Count2/5

35 tools is well past the 25+ threshold for a coherent MCP surface, and the count is padded by near-duplicate query gateways (ask_pipeworx, ask_pipeworx_beta, ask_pipeworx_grounded, deep_research), multiple overlapping polymarket scanners, and memory/subscription utilities that could easily live in separate servers. The broad scope justifies some multiplicity, but the set feels bloated rather than curated.

Completeness4/5

For a read/research data platform, the surface is largely complete: entity resolution, profiles, comparisons, change feeds, grounded lookup, deep research, claim validation, dataset discovery, memory, and subscription lifecycles are all covered. Minor gaps exist—no direct dataset-resource download, no prediction-market execution, and no data write/update operations—but these are workaround-able or outside the server's apparent purpose.

Resources