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Glama

Gnews

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. First observed

TDQS

A4.6/5.0
Behavior5/5

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

Annotations declare readOnlyHint=true and destructiveHint=false; the description adds behavioral details like truncation at 200K chars, BGE-base-en embeddings, cosine similarity over 500-char overlapping windows, and flagging of truncated inputs, far exceeding annotation coverage.

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 efficient with front-loaded purpose: first sentence states core action. Subsequent sentences add usage guidance and technical details without redundancy. Slight room for tighter phrasing, but overall well-structured.

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 no output schema, the description fully explains return format (top-N passages with character offsets and similarity scores), constraints (200K char cap with truncation flag), and pairing with other tools, making it complete for an agent to use 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 coverage is 100%, so baseline is 3. The description adds value with examples for the query parameter ('supply-chain risk', 'fiscal year 2024 revenue') and clarifies the text parameter as 'document text to search inside', enhancing semantics.

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' using specific verbs and resource. It contrasts with fetching whole documents and mentions a sibling tool 'ask_pipeworx_grounded', distinguishing its purpose.

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 explains when to use: 'when the record is too big to cram into the prompt' and 'search_within saves context'. It also suggests pairing with 'ask_pipeworx_grounded'. However, it lacks explicit 'when not to use' instructions.

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
Disambiguation4/5

Most tools have distinct, well-described purposes, but there is some overlap, especially among prediction market tools (bet_research, polymarket_arbitrage, etc.) and between ask_pipeworx and ask_pipeworx_grounded. Agents might occasionally select the wrong tool without careful reading.

Naming Consistency3/5

Tool names follow a mix of snake_case and camelCase (e.g., ai_visibility_check vs discover_tools). Some names are descriptive but inconsistent in style (subscribe, unsubscribe, list_subscriptions). Pattern is not uniform.

Tool Count3/5

With 32 tools, the server covers many domains (news, financials, prediction markets, entity resolution, memory). While each tool has a justification, the count feels heavy for a single server, and some tools could be consolidated.

Completeness3/5

The tool set spans a wide range of data sources and operations, but there are notable gaps. For news, only search and top headlines exist without advanced filtering. Prediction markets lack order placement tools. The broad scope means depth is sacrificed in some areas.

Resources