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F1

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?

Beyond annotations (readOnly, idempotent), the description reveals technical details: BGE-base-en embeddings, cosine similarity, 500-char overlapping windows, 200K char cap with truncation flag, and the fact that each passage includes character offsets.

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 a focused, front-loaded paragraph that efficiently conveys purpose, usage, and technical details without redundant or irrelevant sentences.

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 adequately covers what is returned (passages with offsets and scores), how it works (embedding model, windowing), and constraints (200K cap, truncation flag). No gaps remain.

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 description coverage is 100%, and the description adds value by providing query examples and clarifying the text limit. However, it does not fully explain the impact of the limit parameter beyond what the schema states, keeping this from a perfect score.

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 it performs semantic search inside a fetched record, using specific verbs and resources. It distinguishes itself from siblings like ask_pipeworx_grounded by explaining that it searches inside already-fetched text, not on external data.

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 fit in the prompt. It also provides guidance on pairing with ask_pipeworx_grounded, and implies when not to use (i.e., when the full document can be included directly).

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

A3.6/5.0
Disambiguation2/5

Several tools are near-clones: ask_pipeworx, ask_pipeworx_beta, and ask_pipeworx_grounded differ mainly by mode, and deep_research overlaps with all of them. The five polymarket_* tools plus bet_research also blur together, and discover_tools vs suggest_questions both serve a 'what can I do' purpose.

Naming Consistency3/5

All names are readable snake_case, but the convention is mixed: verb-first (get_current_standings, validate_claim, scan_dependency), noun-first (polymarket_edges, pipeworx_trending), and bare verbs (remember, forget, subscribe). The F1 tools follow a clean get_* pattern that doesn't extend to the rest of the set.

Tool Count2/5

35 tools is excessive, especially since the server is named 'F1' but only 4 tools relate to F1. The set could be consolidated substantially: three ask_pipeworx variants, multiple overlapping polymarket scanners, and two tool-discovery helpers all add weight without clear scope.

Completeness2/5

The F1 side is thin: no qualifying results, constructor standings, lap data, circuits, or driver search by name. The Pipeworx half is broad, but it belongs to a different domain, leaving the overall surface feeling incomplete for either purpose.

Resources