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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?

Discloses beyond annotations: uses BGE-base-en embeddings + cosine over 500-char windows, 200K char cap with truncation and flagging, and each passage has offset for verification. No contradiction with 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?

Single paragraph, front-loaded with purpose, followed by usage context, return format, pairing suggestion, technical details. Every sentence is informative, no fluff.

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, description fully explains return format (passages with offsets and scores), technical constraints (embedding model, window size, char limit), and provides usage pairing. Covers all needed context for an agent.

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 covers all 3 parameters (100% coverage). Description adds extra context: text max size and truncation behavior, query examples, and mentions limit indirectly. Adds value beyond schema but baseline is 3, so 4 due to truncation detail.

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?

Clearly states 'Semantic search INSIDE a fetched record' with specific outputs (passages, offsets, scores). Distinguishes from siblings by pairing with ask_pipeworx_grounded and emphasizing usage when records are too large for prompts.

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?

Explicitly says when to use: 'when the record is too big to cram into the prompt'. Also provides alternative pairing with ask_pipeworx_grounded, indicating when to use that tool instead.

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

A4/5.0
Disambiguation3/5

The tool set includes several overlapping research/lookup tools (ask_pipeworx, ask_pipeworx_grounded, deep_research, entity_profile) and multiple Polymarket tools, which could confuse an agent despite detailed descriptions. The boundaries between these tools are explained in the descriptions, but the sheer number of similar-purpose tools creates ambiguity.

Naming Consistency4/5

Most tools follow a verb_noun pattern (e.g., search_crates, get_versions, validate_claim), but there are exceptions like entity_profile, deep_research, and bet_research, which break the pattern. The overall naming is readable and mostly consistent, with the polymarket_ and pipeworx_ prefixes providing grouping.

Tool Count2/5

With 35 tools, the server is far too heavy for a focused service. The server name 'Crates' suggests a narrow domain, but only 5 tools relate to Rust crates, while the rest cover disparate areas (Pipeworx, Polymarket, memory management). This mismatch and high count make the tool surface unwieldy.

Completeness4/5

For the underlying Pipeworx/Polymarket domain that the majority of tools serve, the coverage is strong: lookup, grounded answers, research, entity profiles, comparisons, changes, claim validation, scanning, memory, subscriptions, tool discovery. Minor gaps exist (e.g., no direct summarization), but the set feels well-rounded for a data analysis agent.

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