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Data360

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

A4.6/5.0
Behavior5/5

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

Adds significant detail beyond annotations: embedding model (BGE-base-en), cosine similarity, 500-char overlapping windows, 200K character cap with truncation flag, and offset verification for quotes. All annotations (readOnlyHint, idempotentHint, etc.) are consistent and expanded upon.

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?

Description is clear and front-loaded with the purpose. It is concise (4 sentences) with each sentence adding value. Could be slightly more structured but overall effective.

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) and limits (200K chars, truncated and flagged). The use case, method, constraints, and complementary tool are all covered, making the tool self-explanatory.

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%, so baseline is 3. Description adds behavioral context (embedding model, similarity metric, windowing, truncation) and usage hints for the query parameter, providing value beyond schema descriptions.

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?

Description clearly states the tool performs semantic search inside a fetched record, with concrete examples (SEC 10-K, article, tool result). Distinguished from siblings by focusing on searching within already-retrieved text, not global 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 large for the prompt, saving context by returning only relevant passages. Mentions pairing with ask_pipeworx_grounded for grounded generation. Does not explicitly state when not to use or list alternatives, but the context is clear.

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

Multiple tools answer factual questions (ask_pipeworx, ask_pipeworx_beta, ask_pipeworx_grounded, validate_claim, deep_research), and ask_pipeworx_beta is explicitly identical to the stable router right now, creating genuine selection ambiguity. Most other clusters—memory, subscriptions, entity research, Polymarket—are reasonably distinct once the verbose descriptions are read.

Naming Consistency4/5

Names are consistently lowercase snake_case with recognizable family prefixes (ask_pipeworx_*, data360_*, polymarket_*, pipeworx_*), which aids grouping. The convention mixes verb-first names like resolve_entity with noun/prefix names like polymarket_edges, but it is still readable and predictable enough.

Tool Count2/5

34 tools is well past the heavy range, and the server bundles several unrelated concerns—universal data routing, prediction-market analytics, memory, subscriptions, AI-visibility marketing, and npm dependency scanning—into one surface. Many tools earn their place, but the aggregate is overloaded and likely to slow tool-selection.

Completeness4/5

The data-research side has strong coverage: discovery, universal routing, grounded verification, entity resolution, profiles, comparisons, recent-changes tracking, and in-record search. Subscription lifecycle and memory are complete, and the prediction-market suite even covers fill-risk and edge persistence; only a few niche read/write operations are absent.

Resources