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Hpo Api

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

A4.8/5.0
Behavior5/5

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

Annotations already declare readOnlyHint=true, openWorldHint=true, idempotentHint=true, destructiveHint=false. The description adds valuable behavioral details: embedding model (BGE-base-en), windowing (500-char overlapping windows), character cap (200K chars with truncation flag). This goes well beyond 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?

Well-structured: purpose first, then usage guidance, then pairing, then technical details. Every sentence adds value. Appropriate length for the tool's complexity.

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?

Despite no output schema, the description specifies return format ('top-N passages with character offsets and similarity scores'). Input constraints, behavioral details, and usage context are all covered. No gaps.

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?

Schema coverage is 100% with descriptions for all three parameters. The description enriches each: text gets 'max ~200K chars' limit, query gets example phrases, limit gets default value. Adds meaningful context 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' and provides concrete examples (SEC 10-K body, article, long tool result) and output details (top-N passages with offsets and similarity scores). It distinguishes from sibling tools by focusing on intra-document search.

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?

Explicitly says 'Use when the record is too big to cram into the prompt' and describes pairing with ask_pipeworx_grounded. While it doesn't list explicit alternatives, the context is clear enough for selecting the 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

A4.1/5.0
Disambiguation3/5

Many tools have distinct purposes, but there is overlap among the Polymarket tools (bet_research, polymarket_edges, polymarket_arbitrage, polymarket_kalshi_spread) and between ai_visibility_check and scan_competitor_ai_presence. Detailed descriptions help, but some tools could still be confused.

Naming Consistency5/5

All tool names follow a consistent verb_noun snake_case pattern (e.g., ai_visibility_check, resolve_entity, validate_claim). No mixing of conventions.

Tool Count2/5

With 27 tools spanning HPO, data queries, betting, memory, and utilities, the server is over-scoped. It aggregates multiple domains that would be better split into separate servers. The count feels excessive for a coherent set.

Completeness4/5

Within each domain (HPO ontology, Pipeworx data, Polymarket betting, etc.), the tool surface is reasonably complete. However, the overall server lacks a single clear purpose, making it hard to assess completeness holistically.

Resources