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Data Providence

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, so the agent knows it's safe and idempotent. The description adds important behavioral details: truncation at 200K chars with flag, embedding model, chunking over 500-char windows, and output format (passages with offsets and scores). No contradictions.

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?

The description is relatively concise given the amount of useful information. It front-loads the core purpose and usage, then adds technical details. A slight reduction in length could improve conciseness, but the structure is 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?

The tool has no output schema, but the description fully explains the return value (top-N passages with character offsets and similarity scores). It covers all necessary context: pairing with sibling tool, embedding model, chunking strategy, character limit, and truncation behavior. Complete for the tool's complexity and lack of output schema.

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 the parameter descriptions are already present. The description adds operational context (e.g., max chars for text, natural-language query examples) and explains the output format, which indirectly adds parameter meaning. However, the parameter descriptions themselves are not significantly enhanced 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 the tool performs semantic search inside a fetched record, specifies the use case (when record is too large for prompt), and contrasts with sibling tools like ask_pipeworx_grounded. It includes technical details (BGE-base-en, cosine, 500-char windows, 200K char cap) that precisely define the tool's functionality.

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 tells when to use ('when the record is too big to cram into the prompt') and provides alternative pairing with ask_pipeworx_grounded. The description gives clear context for when this tool is appropriate versus alternatives.

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

Most tools have distinct purposes, but several clusters overlap: ask_pipeworx vs ask_pipeworx_beta are currently functionally identical, polymarket_arbitrage / polymarket_edges / polymarket_kalshi_spread all hunt mispricings via different mechanisms, ai_visibility_check is wrapped by scan_competitor_ai_presence, and discover_tools vs suggest_questions both serve tool discovery. The rich descriptions mitigate but do not eliminate misselection risk.

Naming Consistency4/5

Names are all snake_case and follow recognizable conventions: verb_noun for actions (compare_entities, resolve_entity, validate_claim), domain-prefixed families (polymarket_*, pipeworx_*, recent_*, ask_pipeworx_*), and a few bare verbs (remember, recall, query). Minor deviations like bet_research (noun_verb) and noun-only names (datasets, metadata) break the pattern, but the overall scheme is predictable.

Tool Count2/5

At 34 tools, this exceeds the 25+ threshold for 'too many' and bundles several distinct domains — general data querying, prediction markets, AI visibility, memory, subscriptions, open data, and npm auditing — into one server. The breadth is defensible for a data platform, but the agent-facing surface is sprawling and would benefit from splitting into focused servers.

Completeness4/5

The core data workflow is well covered: discover (discover_tools, suggest_questions), resolve (resolve_entity), query (ask_pipeworx), ground (ask_pipeworx_grounded, validate_claim), research (deep_research), compare (compare_entities), profile (entity_profile), and changes (recent_changes). Prediction markets, memory, and subscriptions each have full lifecycles. The main gap is no tool for fetching returned pipeworx:// citation URIs directly, plus a few soft-failing sources.

Resources