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Thesportsdb

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.

TDQS

A4.6/5.0
Behavior4/5

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

Annotations already declare read-only, idempotent, open-world. The description adds valuable behavioral context: fans out across multiple sources, returns specific data fields, mentions USPTO sunset soft-fail, and notes names not supported. This goes beyond annotations without contradicting them.

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 but well-organized, front-loading examples and purpose. It could benefit from slight structural formatting (e.g., bullet list of return fields), but remains concise and informative.

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 comprehensively lists return fields (cik, filings, fundamentals, patents, news, LEI) and notes failure modes (USPTO sunset, name not supported). This fully equips an AI agent to understand and invoke the tool correctly.

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 clear descriptions. The description adds concrete examples (ticker 'AAPL', CIK '0000320193') and reinforces the unsupported name constraint, enhancing usability.

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 full cross-source profile of US public companies, with concrete examples ('Tell me about X', 'company profile for Microsoft') and explicitly distinguishes from chaining individual lookups. It also references sibling tool resolve_entity for name resolution.

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 explicitly says when to use ('ALWAYS PREFER over chaining...when the user asks for a holistic view') and when not to ('names not supported – use resolve_entity first'). This is excellent guidance.

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

B3.3/5.0
Disambiguation2/5

The sports tools are distinct, but the majority of the set has heavy overlap: ask_pipeworx, ask_pipeworx_beta, ask_pipeworx_grounded, deep_research, discover_tools, and suggest_questions all serve as query/entry-point tools, with ask_pipeworx and ask_pipeworx_beta explicitly identical. The Polymarket family (bet_research, polymarket_arbitrage, polymarket_edges, polymarket_edge_tracker, polymarket_fill_risk, polymarket_kalshi_spread) also blurs together.

Naming Consistency2/5

The sports subset follows a clean verb_noun pattern (get_player, list_leagues, search_teams), but the rest mixes several naming schemes: ask_pipeworx*, pipeworx_* prefixed tools, polymarket_* tools, one-word verbs (remember, recall, forget), and compound names like scan_competitor_ai_presence. No single consistent convention governs the set.

Tool Count2/5

42 tools is far too many for a server named Thesportsdb; only 10 tools relate to sports data, while 32 belong to an unrelated Pipeworx data/betting/memory platform. The set feels like two or three servers merged into one, making it heavy and unfocused for any single purpose.

Completeness2/5

For the stated sports domain, the surface is partial: you can get teams, players, league tables, and recent/next fixtures, but there are no player statistics, head-to-head records, venue details, or season history — leaving notable gaps. The Pipeworx half is broad but doesn't belong in a server with this name, so the set as a whole is incomplete for its apparent purpose.

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