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Macro Snapshot

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

A5/5.0
Behavior5/5

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

Discloses truncation at 200K chars, embedding model (BGE-base-en), window size, similarity metric, and offset for quote verification. No contradiction with annotations (readOnly, openWorld, idempotent).

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?

Concise yet comprehensive: front-loaded key info, each sentence adds value. 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?

Explains return values (passages with offsets and scores) despite no output schema. Fully covers tool behavior and limitations.

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. Description adds example queries, default limit, and text truncation flag, providing additional meaning beyond 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?

Description clearly states it performs semantic search inside a fetched record, with specific examples (SEC 10-K, article). Distinguishes from sibling ask_pipeworx_grounded by describing how they pair.

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 states when to use: when record is too large for prompt. Mentions sibling tool and provides guidance on pairing with ask_pipeworx_grounded for grounding.

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
Disambiguation2/5

Several tools occupy heavily overlapping space: ask_pipeworx, ask_pipeworx_beta, and ask_pipeworx_grounded share the same router, with the beta version explicitly noted as currently identical to the stable one. The six polymarket_* tools plus bet_research and macro_snapshot/indicator further blur boundaries, so an agent could easily route a query to the wrong entry point despite the detailed descriptions.

Naming Consistency4/5

Names are consistently snake_case and mostly follow a verb_first or domain_prefix pattern (ask_pipeworx, resolve_entity, validate_claim, polymarket_edges). Minor deviations like entity_profile, indicator, macro_snapshot, and recent_alerts use noun phrases, but the style is still predictable and readable.

Tool Count2/5

At 33 tools, the surface is heavy and exceeds the 25+ threshold for 'too many'. While the server covers many domains, several tools are near-duplicates or conveniences (ask_pipeworx_beta, scan_competitor_ai_presence, indicator) that could be consolidated.

Completeness5/5

For its apparent scope—structured data Q&A, grounded research, entity comparison, prediction-market analysis, subscriptions, and memory—the tool set covers the full lifecycle: query, ground, verify, research, compare, monitor, subscribe, alert, and manage state. There are no obvious dead ends; even supporting workflows like discover_tools and suggest_questions are provided.

Resources