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Useragent

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

A4.3/5.0
Behavior5/5

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

The description adds rich behavioral details beyond the annotations, such as the embedding model (BGE-base-en), window size (500-char overlapping), maximum input length (200K chars with truncation and flagging), and output format (passages with offsets and similarity scores). Annotations already declare read-only, open-world, and idempotent hints, and the description is fully consistent with them.

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?

The description is structured with a clear initial purpose statement, followed by usage guidance, technical details, and pairing suggestions. It is informative without being excessively long, though the technical details could be slightly condensed for brevity.

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 (semantic search with specific model, truncation, offsets, and no output schema), the description fully covers behavior, limitations, and output structure. It provides sufficient context for an agent to use the tool correctly, including examples and pairing suggestions.

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 coverage is 100%, and the description enriches the parameter definitions with examples for 'query' (e.g., 'supply-chain risk'), clarifies the 'text' parameter's max length and truncation behavior, and specifies the default and range for 'limit'. This adds meaningful guidance beyond the schema alone.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose4/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states that the tool performs semantic search inside a fetched record, specifying the action ('search within') and the resource ('record text already pulled'). It implicitly distinguishes itself from other tools by focusing on searching within an already-fetched document, but does not explicitly differentiate from similar search tools like ask_pipeworx_grounded, though it pairs with it.

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?

The description explicitly advises using this tool when 'the record is too big to cram into the prompt,' explaining the benefit of saving context and returning only relevant passages. It also suggests pairing with ask_pipeworx_grounded for grounded question answering. However, it does not mention when not to use it or provide alternatives.

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
Disambiguation2/5

ask_pipeworx, ask_pipeworx_beta, ask_pipeworx_grounded, and deep_research all perform overlapping routed-search functions; ask_pipeworx_beta is even documented as currently identical to ask_pipeworx. The three Polymarket discovery tools (polymarket_edges, polymarket_arbitrage, polymarket_fill_risk) also have blurry boundaries around finding vs. validating vs. executing on edges.

Naming Consistency4/5

Nearly all tools use snake_case with a verb_noun or descriptive pattern (ask_pipeworx, validate_claim, resolve_entity, list_subscriptions). Minor deviations exist — bare verbs like remember/recall/forget and noun_first names like bet_research or entity_profile — but the convention is largely predictable and readable.

Tool Count2/5

32 tools is heavy, and the set spans unrelated domains: Pipeworx data routing, prediction-market trading, agent memory, subscription management, npm dependency checks, user-agent parsing, and llms.txt generation. The sub-clusters each earn their place individually, but as a single server surface the count is unjustifiably large and scattershot.

Completeness3/5

The Pipeworx research surface is quite complete (lookup, grounded answers, deep research, entity profiles, comparisons, validation, entity resolution, discovery, feedback), and memory/subscription lifecycles are fully covered. However, the server has no coherent single domain — user-agent parsing (the server's namesake) has only one tool, while unrelated utilities like generate_llms_txt and scan_dependency appear with no supporting ecosystem.

Resources