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Glama

Data Seattle

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
Behavior5/5

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

Annotations declare readOnlyHint, openWorldHint, idempotentHint, and destructiveHint false. The description adds significant behavioral context: fan-out to multiple APIs, GDELT/GNews fallback, PatentsView sunset, and return structure. No contradictions.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

Description is well-structured with examples and parentheticals. Every sentence adds value. Front-loaded with purpose and use cases, then detailed behavior. No wasted words.

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 explains return structure (changes[], total_changes, citation URIs) and handles edge cases like patent API sunset. It is thorough and covers all essential behavioral aspects.

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 decent descriptions. The description adds concrete examples for 'since' (ISO, relative) and 'value' (ticker, CIK), improving usability. Baseline 3, elevated by additional guidance.

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 provides a change feed for a company (SEC filings, news, patents) in a given time window. It distinguishes from sibling 'entity_profile' by specifying use cases. Purpose is specific and well-defined.

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 includes example queries and explicitly says when to use 'entity_profile' instead. It explains fallback behavior for news and patent limitations. However, it doesn't exhaustively list when not to use this tool or other alternatives.

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

A4.1/5.0
Disambiguation3/5

Many tools have distinct purposes, but the ask_pipeworx variants (ask_pipeworx, ask_pipeworx_beta, ask_pipeworx_grounded) and the prediction market tools (bet_research, polymarket_edges, polymarket_arbitrage) can cause confusion due to overlapping functionality. Some tools like 'seattle_recent' are vague.

Naming Consistency4/5

Most tools follow a consistent verb_noun or noun_verb pattern (e.g., validate_claim, resolve_entity). However, a few like 'seattle_recent' and 'pipeworx_trending' deviate slightly, and 'recent_alerts' mixes noun_verb.

Tool Count2/5

34 tools is excessive for a single server, covering data retrieval, prediction markets, Seattle data, memory, subscriptions, and utility. The broad scope feels bloated and overwhelming, making it hard for agents to find the right tool.

Completeness4/5

The server covers a wide array of domains with good depth in data retrieval and prediction markets. Minor gaps exist (e.g., Seattle tools limited to four datasets, no other city data), but overall it addresses most use cases its tools suggest.

Resources