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Glama

Climatiq

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

A4.9/5.0
Behavior5/5

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

Annotations already declare readOnlyHint=true, idempotentHint=true, destructiveHint=false. The description adds valuable behavior: parallel calls, source fallback logic, soft-fail for patents, and return structure. No contradictions 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?

The description is a single paragraph but efficiently packs information. It front-loads example queries and usage patterns. Could benefit from bullet points for clarity, but no unnecessary fluff.

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 no output schema, the description details the return structure (changes[] grouped by source, total_changes count, citation URIs). It also mentions tool context and alternative tool (entity_profile), making it complete for an AI agent.

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 all parameters have descriptions. The description adds further value by explaining the `since` format (ISO or relative), recommending '30d' or '1m', and clarifying the `value` can be ticker or CIK.

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 explicitly states the tool provides a change feed for a company, aggregating from SEC EDGAR, GDELT/GNews, and USPTO. It uses specific verbs ('fans out') and distinguishes from sibling tool 'entity_profile', which provides a 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?

The description gives explicit example queries ('What's new with X', 'latest on Y') and directly states when to use this tool vs. entity_profile. It also explains fallback behavior and parameter semantics clearly.

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

A4/5.0
Disambiguation3/5

Most tools have distinct jobs and the descriptions are unusually detailed with cross-references, but there is real overlap in the ask_pipeworx family (ask_pipeworx_beta is explicitly identical to ask_pipeworx right now), the Polymarket edge/arbitrage cluster, and some company-research tools. An agent can usually pick correctly, but only after reading long descriptions carefully.

Naming Consistency3/5

All names are snake_case and many are clear verb_noun forms like estimate_emissions or list_subscriptions, but the set also contains descriptive noun phrases (entity_profile, recent_changes, ai_visibility_check), brand-prefixed names (pipeworx_feedback, polymarket_edges), and bare memory verbs (remember, recall, forget). This is a readable but mixed convention rather than one predictable pattern.

Tool Count2/5

34 tools is well above the comfortable ceiling, and the set bundles several unrelated domains: Climatiq emissions, Pipeworx data research, prediction-market analytics, memory, subscriptions, AI visibility, llms.txt generation, and npm dependency checks. It feels heavy and redundant, with ask_pipeworx_beta and the AI-visibility pair as candidates for removal or merging.

Completeness4/5

The core query workflows are well covered: emission factors lead into estimation, general lookups have plain/grounded/deep variants, claim validation and entity profiles exist, and subscriptions/memory have full lifecycles. The main gaps are minor—no batch emissions endpoint or direct order execution—so agents can work around them.

Resources