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Glama

Data Sf

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 indicate readOnly, openWorld, idempotent; description adds parallel fan-out, source fallback behavior (GDELT→GNews), soft-fail for USPTO, and return structure details.

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?

Concise and front-loaded with example phrases; every sentence is informative. Slightly dense but 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?

Despite no output schema, description explains the result structure and covers all sources, fallback, and parameter behavior thoroughly for a complex tool.

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 covers all parameters; description adds context for `type` (only company), `since` formats including relative shorthand, and `value` accepting 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 clearly states the tool returns a change feed for a company in a time window, listing specific sources and distinguishing from the sibling entity_profile tool.

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?

Provides explicit example queries, guidance on the `since` parameter (e.g., use '30d' for monitoring), and a clear alternative tool (entity_profile) for static profiles.

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

A3.9/5.0
Disambiguation3/5

Several tools have overlapping purposes: ask_pipeworx, ask_pipeworx_beta, and ask_pipeworx_grounded all route questions to similar data sources, while search_within also overlaps with grounded answering. bet_research, polymarket_edge_tracker, and polymarket_fill_risk all target prediction markets. Agents must read descriptions carefully to pick the right variant.

Naming Consistency4/5

Most tools follow a verb_noun snake_case pattern (ask_pipeworx, bet_research, validate_claim, resolve_entity). Some are single nouns (recent_alerts, recent_changes, key_alerts), a few break the convention (ask_pipeworx_beta, ask_pipeworx_grounded, remember, forget). Overall mostly consistent with minor deviations.

Tool Count2/5

34 tools is a high count for a general-purpose data server, and several seem redundant: ask_pipeworx vs ask_pipeworx_beta vs ask_pipeworx_grounded, polymarket_edge_tracker vs polymarket_arbitrage, and the numerous meta-tools create overhead. A focused dataset server would be better with 10–15 tools.

Completeness4/5

The surface covers many domains well: SEC filings, economics/FRED, prediction markets, news, clinical trials, San Francisco open data, npm dependencies. Obvious gaps include no financial statement form filings beyond 8-K/10-K, no calendar/event scheduling, and no update-else path for several key objects (but memory tools fill that gap). Attribution currently ships in almost all requested tools, providing evidence.

Resources