Skip to main content
Glama

F1

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. Added

TDQS

A4.8/5.0
Behavior5/5

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

Annotations already declare readOnly and idempotent hints, but the description adds crucial behavioral context: fan-out to three sources, fallback from GDELT to GNews, USPTO soft-fail, and return format (changes grouped by source, total_changes, 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.

Conciseness5/5

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

The description is a single paragraph, front-loaded with query examples, then succinctly covers all key behaviors. Every sentence adds necessary detail without redundancy.

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 the tool's complexity (multiple sources, fallback, edge cases), the description is remarkably complete: it covers sources, fallback, date formats, return structure, and sibling alternative. No output schema needed as description covers return format.

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?

With 100% schema coverage, the description still adds value: examples for `since` (ISO and relative), recommendation for '30d' or '1m', explains `value` accepts ticker or CIK, and confirms `type` only supports 'company'.

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 fetches a change feed for a company, covering SEC filings, news, and patents, with specific query examples. It distinguishes from sibling `entity_profile` by noting the static vs. change feed difference.

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 provides explicit guidance to use `entity_profile` for static profiles, and details the scope and sources. However, it lacks explicit when-not-to-use scenarios beyond that.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

All F1 toolsInspect
Try in Browser
RayrunBuild, deploy, and operate MCP servers without becoming an infrastructure team.ray.runAd

Glama MCP Gateway

Add one secure layer between your agents and this server.

TDQS

A3.7/5.0
Disambiguation3/5

Most tools have detailed 'use when' guidance and the polymarket/entity clusters are distinguishable, but ask_pipeworx and ask_pipeworx_beta are currently identical, and ask_pipeworx/ask_pipeworx_grounded/deep_research sit close together. Many other tools (ai_visibility_check vs scan_competitor_ai_presence, bet_research vs polymarket_edges) require careful reading to keep separate.

Naming Consistency3/5

Consistent snake_case and recognizable subfamilies (ask_pipeworx*, polymarket_*, get_*) keep names readable. However the overall set mixes verb_noun (get_schedule, resolve_entity), bare verbs (remember, forget), noun phrases (recent_alerts, pipeworx_trending), and compound noun names (bet_research, polymarket_fill_risk), so there is no single predictable convention.

Tool Count2/5

35 tools is well over the 25+ threshold and spans many unrelated concerns: F1 data, universal data lookup, prediction markets, subscriptions, memory, npm scanning, and AI visibility. While a broad data platform can justify a large surface, the mix of one-off and meta tools makes this feel bloated rather than well-scoped.

Completeness2/5

For a server named F1 the surface is thin: it covers schedule, driver profiles, race results, and driver standings but lacks constructor standings, qualifying, team/circuit data, and a driver list. The surrounding Pipeworx tools provide depth in other domains, but they do not fill the F1-specific gaps.

Resources