Skip to main content
Glama

Scream Void

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

A4.7/5.0
Behavior5/5

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

Adds behavioral details beyond annotations: embedding model (BGE-base-en), chunking (cosine over 500-char overlapping windows), character cap (200K chars with truncation flag), and offset for verification. 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.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

Concise but informative: opens with purpose, then usage guidance, then technical details. Every sentence adds value with no 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?

Given the tool's moderate complexity and rich annotations, the description covers purpose, use cases, technical behavior, and limits. No output schema exists, but return format (passages with offsets/scores) is explained adequately.

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 description coverage is 100% for all 3 parameters (text, query, limit). The description does not add significant new meaning beyond the schema descriptions, so baseline 3 is appropriate.

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 'Semantic search INSIDE a fetched record' with a specific verb and resource, and distinguishes itself from siblings by mentioning 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 says 'Use when the record is too big to cram into the prompt' and 'Pairs with ask_pipeworx_grounded' for an alternative workflow, providing clear when-to-use and when-not-to-use guidance.

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

All Scream Void 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
Disambiguation2/5

Many tools overlap significantly in purpose, especially the ask_pipeworx variants (standard, beta, grounded) and deep_research, as well as the polymarket arbitrage/edges/fill_risk/kalshi_spread suite. It would be hard for an agent to reliably choose the correct tool without deep understanding of subtle distinctions.

Naming Consistency3/5

Tool names mostly use snake_case, but there is no consistent prefix or verb pattern. Some names are descriptive phrases (e.g., scream_void_scream, compare_entities) while others are vague (e.g., forget, recall). The mix of 'pipeworx_' prefix on some tools and lack of it on others adds inconsistency.

Tool Count3/5

32 tools is on the high side for a data research server, given the overlapping functionality. Some tools could be merged (e.g., the ask_pipeworx variants, polymarket tools). However, the count is not excessive enough to be unmanageable, and each tool serves a specific niche.

Completeness4/5

The server covers a broad range of data sources and prediction market analysis, with tools for research, comparison, monitoring, and memory. Minor gaps exist (e.g., no tool to update stored memories or manage subscriptions beyond CRUD), but core workflows are well-supported.

Resources