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Mlb Stats

by io.github.pipeworx-io

Search Within a Source

search_within
Read-onlyIdempotent

Semantic search INSIDE a fetched record. Pass the text you already pulled (e.g. a SEC 10-K body, an article, a long tool result) plus a natural-language query; get back the top-N passages with character offsets and similarity scores. Use when the record is too big to cram into the prompt — search_within saves context, returns only the passages that matter, and every passage carries an offset so the agent can verify a verbatim quote. Pairs with ask_pipeworx_grounded: fetch with the gateway, ground over the relevant passages instead of the whole document. BGE-base-en embeddings + cosine over 500-char overlapping windows; cap is 200K chars (longer inputs are truncated and flagged).

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
textYesThe document text to search inside (max ~200K chars).
limitNoMax passages to return (1-20, default 5).
queryYesNatural-language query — what passages do you want? E.g. "supply-chain risk", "fiscal year 2024 revenue", "drug interactions with warfarin".

TDQS

A4.9/5.0
Behavior5/5

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

Beyond annotations (readOnlyHint, idempotentHint), the description discloses embedding model (BGE-base-en), chunking strategy (500-char overlapping windows), character cap (200K chars), truncation behavior, and offset details for verbatim quotes.

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 compact and well-structured: purpose, usage, technical details. Every sentence adds value without redundancy. Front-loaded with the key action.

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 tool with 3 parameters, full schema coverage, and no output schema, the description covers what the tool does, when to use it, technical details, and pairing with sibling tools. It is fully sufficient.

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 descriptions. The description adds value by providing natural-language query examples (e.g., 'supply-chain risk') and clarifying the limit default (5). This enhances understanding 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 clearly states 'Semantic search INSIDE a fetched record' with a specific verb and resource. It distinguishes itself from siblings like ask_pipeworx_grounded by explaining that it searches within a provided text.

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 states when to use: when the record is too big for the prompt, and pairs with ask_pipeworx_grounded for grounding. It provides clear guidance and an alternative tool.

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.

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