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Glama

Sistat Si

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

A4.4/5.0
Behavior5/5

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

Goes beyond annotations: specifies embedding model (BGE-base-en), similarity metric (cosine), window size (500-char overlapping), character limit (200K with truncation flag). All annotations are consistent and description adds valuable operational details.

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?

Four sentences, each serving a purpose: purpose, use case, pairing, and technical details. Front-loaded with core functionality. Slightly verbose but efficient for the information delivered.

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 no output schema, the description adequately explains the return format (passages, offsets, scores). Covers parameters, behavior, limits, and integration with sibling tools. No gaps.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters3/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema coverage is 100% with each parameter described. The description adds example queries for 'query' and hints at the character cap for 'text', but these are mostly redundant with schema descriptions. No significant new semantic information beyond what the schema provides.

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 does semantic search inside a fetched record, gives concrete examples (SEC 10-K, article), and specifies the output (top-N passages with offsets and scores). It implicitly distinguishes from siblings like ask_pipeworx_grounded by showing how they work together.

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 tells when to use: when the record is too large for the prompt. Mentions pairing with ask_pipeworx_grounded for grounding. Does not provide explicit 'when not to use' but context is sufficient.

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

A3.9/5.0
Disambiguation4/5

Most tools have clearly distinct purposes, but there is some overlap among ask_pipeworx, ask_pipeworx_grounded, and deep_research, as well as between discover_tools and suggest_questions. However, detailed descriptions help differentiate them.

Naming Consistency2/5

Naming conventions are highly inconsistent, mixing snake_case (ask_pipeworx, forget), camelCase (discoverTools, suggestQuestions), and underscores (ai_visibility_check, compare_entities). No predictable pattern.

Tool Count3/5

33 tools is on the high side, but the broad domain (finance, pharma, prediction markets, etc.) partly justifies it. However, some tools like forget, remember, recall seem generic and could be separated.

Completeness4/5

The tool surface covers a wide range of functionalities: visibility checks, pipeworx queries, entity profiles, comparisons, subscriptions, memory, and more. Minor gaps may exist in real-time data or specific niche sources.

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