Skip to main content
Glama

nouel

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

A4.8/5.0
Behavior5/5

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

Annotations already declare read-only, idempotent, non-destructive behavior. The description adds non-obvious details: BGE-base-en embeddings, 500-char overlapping windows, the 200K char truncation with a flag, and the exact return fields (character offsets, similarity scores). No contradiction with annotations.

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 front-loaded with purpose in the first sentence and remains information-dense throughout. It is slightly verbose due to technical specifics (embedding model, window size) but each sentence contributes to behavioral transparency or usage guidance; appropriately sized overall.

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?

With no output schema, the description fully discloses the return format (passages with offsets and similarity scores), edge cases (truncation over 200K chars), and operational context (pairing with ask_pipeworx_grounded). This is sufficient for an agent to select and invoke the tool correctly.

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 description coverage is 100% for all three parameters, providing a solid baseline. The tool description adds context by clarifying that 'text' should be already-fetched content (e.g., SEC 10-K, article) and reinforcing the natural-language nature of 'query' with examples, which aids correct invocation.

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 a specific verb and resource: 'Semantic search INSIDE a fetched record.' It clearly differentiates from siblings by emphasizing it operates on already-fetched text, not on external sources, and states the output (top-N passages with offsets and scores).

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 says 'Use when the record is too big to cram into the prompt' and recommends pairing with ask_pipeworx_grounded: 'fetch with the gateway, ground over the relevant passages instead of the whole document.' This provides both when-to-use and an alternative approach.

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

All nouel toolsInspect
Try in Browser
RayrunBuild, deploy, and operate MCP servers without becoming an infrastructure team.ray.runAd

Glama MCP Gateway

Add one secure layer between your agents and this server.

TDQS

A3.8/5.0
Disambiguation3/5

Several tool groups have overlapping purposes (e.g., ask_pipeworx, ask_pipeworx_grounded, deep_research all query Pipeworx data; polymarket_arbitrage, polymarket_edges, polymarket_edge_tracker all analyze prediction markets). Descriptions help distinguish them, but the boundaries are not always clear.

Naming Consistency3/5

Tool names are mostly descriptive but mix conventions: some are verb_noun (list_subscriptions, get_prizes_by_year), others are noun_verb (pipeworx_feedback, polymarket_edges), and a few are single verbs (remember, recall). No strong pattern, but still readable.

Tool Count4/5

With 32 tools, the server covers a wide range of domains (data querying, prediction markets, company analysis, Nobel prizes, memory, subscriptions). The count is high but each tool serves a specific purpose, justifying the breadth.

Completeness4/5

The tool set provides comprehensive coverage for its stated domains: data retrieval, entity resolution, comparison, monitoring, and memory. Minor gaps exist (e.g., no direct bet placement on Polymarket), but core workflows are well-supported.

Resources