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Glama

Openf1

by io.github.pipeworx-io

Entity Profile

entity_profile
Read-onlyIdempotent

"Tell me about X" / "research Acme" / "brief me on Tesla" / "what does Apple do" / "company profile for Microsoft" / "give me the rundown on NVDA" / "everything you know about $TICKER" — full cross-source profile of a US public company in ONE parallel call. ALWAYS PREFER over chaining single-pack SEC/XBRL/news lookups when the user asks for a holistic view. Fans out across SEC EDGAR, XBRL, USPTO patents, federal contracts (USAspending), FDA-licensed biologics (Purple Book), H-1B hiring (DOL LCA), news and GLEIF, and returns: cik + company_name (+ resolved_from/resolved_to when value was a name); recent_filings (up to 5 with pipeworx://edgar/company/{cik}/filings/{accession} URIs); fundamentals (LATEST 10-K Revenues + NetIncomeLoss + Cash, sorted period_end DESC); patents (USPTO PatentsView API sunset May 2025 — soft-fails until reactivated); federal_contracts (USAspending awards where the company is the recipient); fda_products (FDA-licensed biologics — vaccines, cell/gene therapies — from the Purple Book; a company with only small-molecule/generic drugs will show none here, that is expected, not a failure); hiring (H-1B sponsorship volume + salary range from DOL LCA filings); recent news mentions via GDELT→GNews fallback; LEI via GLEIF. sources_used / sources_failed say which of these actually returned data for THIS company — an empty section is a real "no data", not a bug. Pass a ticker ("AAPL"), zero-padded CIK ("0000320193"), OR a company name ("Moderna") — names now resolve via SEC EDGAR's company-name match; a private company (no CIK/ticker) returns resolved:false with an explicit notes line, not a bare failure. type accepts "company" or "ticker" interchangeably — both take the same value shapes above.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
typeYes"company" or "ticker" — both are accepted and behave identically; `value` can be a ticker, CIK, or company name either way. person/place coming soon.
valueYesTicker (e.g., "AAPL"), zero-padded CIK (e.g., "0000320193"), or company name (e.g., "Moderna") — names resolve via SEC EDGAR company-name match.

Schema Changelog

Changes observed during successful MCP inspections. Dates show when Glama detected each change.

  1. Changed3 schema fields changed
    • changedInput schema / properties / type / description
      Previous value: -"Entity type. Only \"company\" supported today; person/place coming soon."New value: +"\"company\" or \"ticker\" — both are accepted and behave identically; `value` can be a ticker, CIK, or company name either way. person/place coming soon."
    • changedInput schema / properties / type / enum
      Previous value: -[
-  "company"
-]New value: +[
+  "company",
+  "ticker"
+]
    • changedInput schema / properties / value / description
      Previous value: -"Ticker (e.g., \"AAPL\") or zero-padded CIK (e.g., \"0000320193\"). Names not supported — use resolve_entity first if you only have a name."New value: +"Ticker (e.g., \"AAPL\"), zero-padded CIK (e.g., \"0000320193\"), or company name (e.g., \"Moderna\") — names resolve via SEC EDGAR company-name match."
  2. First observed

TDQS

A4.8/5.0
Behavior5/5

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

Annotations already declare readOnly/openWorld/idempotent/destructive hints, so the bar is lower, but the description still adds substantial behavioral context: it fans out across multiple named sources, returns specific sections, treats empty sections as real 'no data' rather than bugs, and explains how name resolution and private-company cases behave. 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?

The description is dense and long, but nearly every clause carries useful information for a complex multi-source tool. It is front-loaded with trigger examples and key guidance. It loses a point for being one rambling paragraph with heavy semicolon use, which makes parsing harder than a structured breakdown would allow.

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?

With no output schema, the description carries the full burden of explaining return behavior, and it delivers: it enumerates the returned fields, names the data sources, explains resolved_from/resolved_to, provides URIs for filings, and explicitly covers the empty-section case. The private-company fallback and sources_used/sources_failed semantics are also disclosed, making the tool's behavior highly predictable.

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 description coverage is 100%, so the baseline is 3. The description goes beyond the schema by adding concrete examples ('AAPL', '0000320193', 'Moderna'), clarifying that both type values behave identically, and disclosing the private-company resolved:false behavior. This adds meaningful decision-relevant semantics beyond the 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?

The description opens with concrete example queries and states the exact deliverable: 'full cross-source profile of a US public company in ONE parallel call.' It clearly names the resource (US public companies) and distinguishes itself from single-pack SEC/XBRL/news lookups, so an agent can identify when this specific tool is intended.

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?

It explicitly says 'ALWAYS PREFER over chaining single-pack SEC/XBRL/news lookups when the user asks for a holistic view,' giving a clear when-to-use rule and naming the alternative pattern it replaces. It also specifies valid inputs (ticker, CIK, company name) and the private-company edge-case behavior, which further guides correct invocation.

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
Disambiguation2/5

Several tool clusters have unclear boundaries: ask_pipeworx, ask_pipeworx_beta, ask_pipeworx_grounded, and deep_research all route questions across the same 5,756-tool catalog, and ask_pipeworx_beta is explicitly identical to ask_pipeworx right now. The polymarket_* family also overlaps heavily (edges, arbitrage, fill_risk, kalshi_spread all surface tradeable discrepancies), and entity_profile/compare_entities/recent_changes all fan out to overlapping SEC/news/patent sources. Descriptions are detailed, but the set contains intentional near-duplicates that make selection genuinely ambiguous.

Naming Consistency2/5

Naming follows no single convention: get_drivers/get_laps/get_meetings/get_sessions use verb_noun, polymarket_arbitrage/polymarket_edges are noun-phrase domain prefixes, pipeworx_feedback/pipeworx_trending use a vendor prefix, remember/recall/forget are bare verbs, and ask_pipeworx variants mix with action phrases like discover_tools, deep_research, and validate_claim. The inconsistency makes it harder to predict related tool names.

Tool Count2/5

35 tools is heavy, and the count is severely mismatched to the server name 'Openf1': only 4 of 35 tools (get_drivers, get_laps, get_meetings, get_sessions) are actually F1-related, while the other 31 are a general-purpose Pipeworx data/prediction-market platform. The number itself could be reasonable for a broad data hub, but for an F1 server it is bloated with unrelated functionality.

Completeness2/5

For the stated F1 domain, the surface is severely incomplete: it covers meetings, sessions, drivers, and laps but lacks race results, qualifying results, standings, pit stops, or constructor data — major gaps for any F1 use case. The Pipeworx side is far more complete (query, research, entities, verification, memory, subscriptions), but that completeness doesn't serve the server's apparent purpose.

Resources