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Mlb Stats

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

A4.8/5.0
Behavior5/5

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

The description goes well beyond the annotations by disclosing fan-out behavior, return fields, source failure semantics, soft-fails (USPTO sunset), empty-section expectations, and resolution behavior. This is exactly the kind of behavioral context an agent needs to interpret results correctly.

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 long, but the tool is genuinely complex and the length is mostly earned. It front-loads the strongest guidance (prefer over chaining) and then systematically enumerates return sections; a mild structural improvement would be breaking the long paragraph into bullets, but current structure is still navigable.

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?

For a complex, multi-source tool with no output schema, the description is remarkably complete: it explains input forms, resolution behavior, expected empty results, failure softness, and the precise meaning of each returned section. An agent can invoke this tool and interpret its response without additional context.

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?

The schema already covers the two parameters at 100%, so the baseline is 3; the description adds value by clarifying private-company resolution behavior and reiterating the interchangeability of `type`. It mostly repeats schema examples, but the resolved:false nuance is meaningful for proper use.

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 states a specific verb/resource: "full cross-source profile of a US public company in ONE parallel call," with concrete example phrasings. It clearly distinguishes itself from single-source lookups and sibling research tools by emphasizing the holistic, parallel fan-out behavior.

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?

Contains an explicit routing rule: "ALWAYS PREFER over chaining single-pack SEC/XBRL/news lookups when the user asks for a holistic view." It also clarifies expected behavior for private companies (resolved:false), which helps an agent decide whether the tool succeeded or the input is simply out of scope.

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

A3.7/5.0
Disambiguation2/5

Severe overlap between entry points: ask_pipeworx and ask_pipeworx_beta are explicitly identical right now, and the prediction-market cluster (polymarket_edges, polymarket_arbitrage, polymarket_edge_tracker, polymarket_fill_risk) has fuzzy boundaries that would confuse an agent picking one. Entity lookups (entity_profile, compare_entities, recent_changes, validate_claim) also overlap on company data. The stakes are raised by the server being named 'Mlb Stats' while most tools are unrelated general-data tools, compounding misselection risk.

Naming Consistency2/5

There is internal consistency within families — the 7 MLB tools share a clean get_ prefix, and the Poly tools share a polymarket_ prefix — but the overall set mixes bare verbs (remember, recall, forget), prefixed families (ask_pipeworx_*), and descriptive compounds (ai_visibility_check, generate_llms_txt) with no unifying convention. The 'Mlb Stats' server name bears no relation to the dominant ask_pipeworx/deep_research naming, which further breaks pattern expectations.

Tool Count2/5

38 tools is well past the heavy threshold, and more importantly the bulk of them (SEC filings, FRED economics, Polymarket arbitrage, npm scanning, AI visibility, subscriptions) have nothing to do with the server's stated MLB purpose. Only 7 of 38 tools are actually baseball-related, so the count is both too high for the labeled scope and misallocated.

Completeness3/5

The 7 MLB tools cover teams, rosters, schedule/scores, standings, player profiles, season stats, and batter-vs-pitcher history — a solid read-only core. However, obvious gaps remain: no per-game box scores, no league leaders, no team offensive/pitching stats, no live game detail beyond final scores. The Pipeworx catch-all router technically fills data gaps but leaves the MLB-specific surface incomplete.

Resources