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Glama

Edamam

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 already show safe read-only, idempotent behavior. Description adds algorithmic details (BGE-base-en embeddings, cosine, 500-char windows), truncation cap (200K chars with flag), and output structure (passages with offsets and scores), exceeding annotation coverage.

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?

Highly concise and front-loaded: first sentence captures the core purpose. Every sentence adds essential information without redundancy.

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 no output schema, the description fully explains return values (passages with offsets and similarity scores), limits (200K chars, truncation), and pairs with a sibling tool. Complete for a semantic search tool.

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 covers all 3 parameters with descriptions. The description adds contextual meaning: 'text' is the fetched record, 'query' is natural-language with examples, 'limit' controls passage count. It also explains that passages carry offsets for verification, adding value 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?

The description clearly states the tool performs semantic search inside a fetched record, with examples of use cases (SEC 10-K, articles) and distinguishes itself from siblings by noting it pairs with ask_pipeworx_grounded for grounding over passages.

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 the record is too big to cram into the prompt') and provides an alternative (ask_pipeworx_grounded), giving clear usage context.

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

The tool set includes multiple overlapping tools (ask_pipeworx variants, many polymarket tools) that serve similar purposes, and there is a sharp domain split between food tools and Pipeworx data tools, making it hard for an agent to choose correctly.

Naming Consistency2/5

Tool names are inconsistent, mixing verb_noun (search_food), noun_verb (nutrition_analysis), prefixed (pipeworx_feedback, polymarket_arbitrage), and no pattern. Some use underscores, some use whole words.

Tool Count3/5

34 tools is on the high side, and the scope is extremely broad (food, data queries, prediction markets, subscriptions), which could overwhelm an agent, but the number alone is not extreme.

Completeness2/5

As a food API server, it is missing key features like recipe details, ingredient substitution, or meal planning, while including many extraneous tools. The data analytics tools are extensive but not aligned with the server's stated purpose.

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