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zippopotam

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?

The description discloses substantial behavioral details beyond the annotations: returns passages with character offsets and similarity scores, uses BGE-base-en embeddings with cosine over 500-char overlapping windows, and has a 200K char cap with truncation and flagging. This provides rich context beyond the readOnly/idempotent hints.

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 well-structured and front-loaded: the opening sentence gives the core purpose, followed by usage guidance, then technical specifics. Every sentence earns its place—no fluff, and it remains informative despite its length.

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 the tool's complexity and lack of output schema, the description is remarkably complete. It explains the return format (passages with offsets and similarity scores), the algorithm, the input cap and truncation behavior, and how to integrate with a sibling tool. This covers all essential aspects for an agent to use it 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?

Although the schema already describes all parameters (100% coverage), the description adds meaningful context: it explains the 'text' parameter as 'the text you already pulled' with examples, gives concrete query examples, and clarifies that 'limit' controls 'top-N passages' with default 5. This enhances the schema definitions.

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's function: 'Semantic search INSIDE a fetched record,' with a specific verb (search) and resource (record). It distinguishes from siblings by emphasizing the 'inside' scope and explicitly pairing with ask_pipeworx_grounded, making its purpose unmistakable.

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?

Explicit guidance is given: 'Use when the record is too big to cram into the prompt' and 'Pairs with ask_pipeworx_grounded: fetch with the gateway, ground over the relevant passages instead of the whole document.' This clearly states when to use it and how it relates to an alternative.

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

A3.8/5.0
Disambiguation2/5

Several tools have overlapping purposes: ask_pipeworx, ask_pipeworx_beta, ask_pipeworx_grounded, deep_research, validate_claim, and bet_research all route questions to the same underlying engine, and ask_pipeworx_beta is explicitly identical to ask_pipeworx right now. The polymarket_* family also has many closely-related entry points, though descriptions do help differentiate them.

Naming Consistency4/5

Names consistently use snake_case with descriptive verb-first patterns (ask_, lookup_, scan_, validate_, resolve_, subscribe) and a clear polymarket_ family prefix. Minor inconsistency exists between lookup_city/lookup_zipcode and resolve_entity, and between noun-style names like entity_profile vs verb-style names like compare_entities, but the overall style is predictable.

Tool Count2/5

33 tools is heavy for a single server, and multiple could be consolidated: the ask_pipeworx variants and deep_research largely overlap, and the memory/subscription categories could be collapsed. For a data-platform gateway the breadth is arguably justified, but the visible redundancy makes the surface feel bloated rather than well-scoped.

Completeness4/5

The core domains are well covered: question answering has multiple modes, entity lookup has resolution and profiling, prediction markets have research/edge/arb/fill-risk coverage, and the memory (remember/recall/forget) and subscription (subscribe/list/unsubscribe/recent_alerts) lifecycles are complete. Minor gaps exist such as no direct tool to fetch a pipeworx:// record by URI, relying instead on MCP resources.

Resources