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Arcgis Marioncountyor

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?

Annotations indicate readOnly/openWorld/idempotent/non-destructive. The description adds non-obvious behavior: truncation at 200K chars with a flag, use of BGE-base-en embeddings with cosine over 500-char windows, and output details (offsets, similarity scores). These are not derivable from 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?

The description is front-loaded with a precise one-line purpose, then appends usage and technical details in five dense sentences. No fluff; each sentence adds a distinct value.

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 having no output schema, the description fully characterizes the return payload (passages, offsets, similarity scores) and handles edge cases (input too large, truncation flag). It also connects to ask_pipeworx_grounded for broader workflow context.

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?

The input schema already describes all three params with 100% coverage. The description enhances this with concrete examples (SEC 10-K, article), query examples ('supply-chain risk'), and clarifies that limit controls 'top-N' passages, adding context 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 opens with 'Semantic search INSIDE a fetched record,' giving a specific verb and resource. It further differentiates from siblings like ask_pipeworx_grounded by emphasizing that the text is already pulled, not fetched by the tool.

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?

It explicitly states a clear use case: 'Use when the record is too big to cram into the prompt.' It also names the companion tool ask_pipeworx_grounded and explains how to combine them, giving both when-to-use and an alternative.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

All Arcgis Marioncountyor toolsInspect
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TDQS

A4.1/5.0
Disambiguation5/5

Each tool has a clearly distinct purpose, with detailed descriptions that specify when to use each. Even overlapping functions like ask_pipeworx varieties are well-differentiated by mode (casual vs grounded vs multi-source).

Naming Consistency4/5

Most tool names follow a verb_noun snake_case pattern (e.g., query_layer, resolve_entity), but a few deviate with single-word verbs (forget, remember, recall) or noun_noun (layer_info). The pattern is mostly consistent with minor exceptions.

Tool Count3/5

With 33 tools, the count is high and borders on heavy. However, the tools span multiple domains (GIS, financial data, prediction markets, memory, subscriptions), and each serves a unique role, so the count is justifiable but could be streamlined.

Completeness4/5

The tool set covers a broad range of data access and analysis tasks relevant to the inferred domain of a multi-purpose assistant. While the ArcGIS portion is limited, the overall surface is well-populated with few obvious gaps.

Resources