Skip to main content
Glama

uooks

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".

Schema Changelog

Changes observed during successful MCP inspections. Dates show when Glama detected each change.

  1. Added

TDQS

A4.9/5.0
Behavior5/5

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

Annotations already declare read-only, idempotent, and non-destructive behavior. The description adds significant extra context: it reveals the embedding model (BGE-base-en), similarity metric (cosine), window size (500-char overlapping), and the 200K character cap with truncation flagging. These details go well beyond the annotation hints and clarify the tool's internal behavior.

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?

The description is four sentences, each earning its place: it states the core function, gives usage guidance, explains the complementary tool, and specifies technical constraints. It is front-loaded with the main purpose and contains no filler or repetition.

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 having no output schema, the description explains what the tool returns (passages with character offsets and similarity scores), defines input limits (200K chars with truncation), and describes the intended workflow with ask_pipeworx_grounded. This gives a complete understanding of the tool's behavior and integration points.

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?

The input schema provides full coverage with clear descriptions for all three parameters. The description adds value by giving concrete examples of the 'text' parameter (SEC 10-K body, article, long tool result) and clarifying the 'query' parameter as natural-language. However, it does not significantly enrich the 'limit' parameter beyond what the schema already states.

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 uses a specific verb and resource: 'Semantic search INSIDE a fetched record.' It clearly explains the tool's function (search within user-provided text) and distinguishes it from sibling tools like search_books and ask_pipeworx. The output is also specified (top-N passages with character offsets and similarity scores), making the purpose unambiguous.

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?

The description explicitly states when to use the tool: 'Use when the record is too big to cram into the prompt.' It also explains how it relates to ask_pipeworx_grounded, describing a clear workflow: fetch with the gateway, then ground over relevant passages. This provides strong usage context and differentiates it from alternatives.

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

All uooks 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

A4.1/5.0
Disambiguation3/5

Several tool families overlap at the boundaries: ask_pipeworx, ask_pipeworx_beta, ask_pipeworx_grounded, and deep_research all route questions to the same 5,724 tools, and ask_pipeworx_beta is currently functionally identical to ask_pipeworx. The Polymarket family is large but each member has a fairly distinct role (research vs. edge scan vs. fill risk vs. tracking); the memory trio and book tools are clear.

Naming Consistency3/5

Most tools follow a snake_case verb_noun pattern (get_book, create, search_books, resolve_entity, list_subscriptions), but there are notable exceptions: recall/remember/forget are bare verbs without a domain prefix, ask_pipeworx begins with a verb but doesn't follow the noun-object structure, and ai_visibility_check/generate_llms_txt break the pattern. It's readable and mostly predictable, but not uniform.

Tool Count3/5

35 tools is heavy and exceeds the typical well-scoped range, but the server is a meta-platform exposing a universal data router plus prediction-market analysis, book lookup, memory, subscriptions, and several composite research tools. Each tool appears to earn its place, though the set feels sprawling and would benefit from consolidation of the ask_pipeworx variants.

Completeness4/5

Coverage is thorough within the apparent domains: data lookup has multiple tiers (casual, grounded, deep research, claim validation), the Polymarket workflow is complete from research to edge discovery to fill-risk verification, memory has save/retrieve/delete, and subscriptions have create/list/cancel/pull. Minor gaps exist (e.g., book author search by name only via Open Library key, no direct tool for invoking a specific raw data pack), but nothing that would strand an agent.

Resources