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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

A4.6/5.0
Behavior5/5

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

Discloses embedding model (BGE-base-en), chunking (500-char windows), cap (200K chars with truncation flag), and return info (offsets, similarity scores). Annotations already indicate safety; description adds valuable technical detail.

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?

Concise (5 sentences), front-loaded with main purpose. Technical details are included but still efficient. Slightly dense but not overly verbose.

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?

Complete for a search tool: explains inputs, outputs (passages with offsets), limitations (cap), and pairing with sibling. No output schema needed as returns described.

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%, so baseline 3. Description adds example queries for 'query' and mentions default for 'limit', but does not add significant new meaning beyond schema descriptions.

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 'Semantic search INSIDE a fetched record' with specific verb and resource. Distinguishes from sibling ask_pipeworx_grounded.

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 tells when to use: when record is too big to prompt. Mentions alternative: ask_pipeworx_grounded for grounding over passages.

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

B3.3/5.0
Disambiguation3/5

The tool set contains multiple groups with overlapping purposes (e.g., ask_pipeworx/ask_pipeworx_grounded/deep_research for data queries, multiple Polymarket tools, and memory tools). However, detailed descriptions help differentiate them, so ambiguity is moderate but not severe.

Naming Consistency2/5

Naming conventions are inconsistent: some tools follow verb_noun (ask_pipeworx, compare_entities), others use domain-prefixed noun_verb (ghg_emissions_by_sector, polymarket_arbitrage). This mix, combined with a server name that doesn't match the tool domain, makes the naming pattern unclear.

Tool Count1/5

With 35 tools, the server is overloaded, especially given its name 'Epa Emissions' which suggests a narrow focus. Only 5 tools (ghg_*, tri_*) are related to emissions; the rest are unrelated, making the count inappropriate for the server's assumed purpose.

Completeness2/5

For an EPA/emissions server, the tool set is incomplete: it lacks other emissions data (e.g., air quality, water quality, enforcement). The inclusion of many unrelated tools (e.g., Polymarket, memory) does not compensate for missing core emissions coverage.