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Glama

Gistemp

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 indicate read-only, open-world, idempotent, non-destructive. Description adds fan-out to multiple sources, GDELT→GNews fallback, USPTO soft-fail, and return structure with citation URIs. No contradiction with annotations.

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?

Single paragraph front-loaded with example queries. Every sentence contributes information. Could be slightly more structured (e.g., bullet points) but remains readable and 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?

Description covers return format (changes[], total_changes, URIs), data sources, fallback behavior, and parameter usage. No output schema exists, so explanation of return values is adequate. Complete for the tool's complexity.

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?

100% schema coverage sets baseline at 3. Description adds extra meaning: `since` accepts ISO or relative shorthand with examples, and recommends '30d' or '1m' for monitoring. This provides practical guidance 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?

Description clearly states the tool provides a change feed for a company over a recent time window, using example queries. It specifies sources (SEC EDGAR, GDELT/GNews, USPTO) and distinguishes from sibling entity_profile 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?

Explicitly says when to use (for 'what's new' queries) and when to use entity_profile instead. Provides parameter guidance for `since`. Does not exhaustively list when not to use, but the alternative is clear.

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

A3.5/5.0
Disambiguation3/5

Many tools have distinct purposes, but there are overlapping functions (e.g., ask_pipeworx vs ask_pipeworx_grounded, entity_profile vs compare_entities vs recent_changes) that could confuse an agent. However, detailed descriptions help mitigate ambiguity.

Naming Consistency2/5

Tool names follow no consistent pattern: snake_case (ai_visibility_check), camelCase (none here but inconsistent), and mixed verb/noun styles (subscribe vs entity_profile). The naming is chaotic and does not follow a predictable convention.

Tool Count2/5

With 33 tools, the server is bloated for its claimed Gistemp focus (only 3 tools directly about Gistemp). Most tools belong to a broader Pipeworx platform, making the scope too broad and unfocused.

Completeness1/5

For a server named 'Gistemp,' the tool set is severely incomplete—only 3 tools cover temperature anomalies. The majority of tools address unrelated domains (Polymarket, SEC filings, memory, etc.), leaving obvious gaps for the stated purpose.

Resources