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ask

Send a self-contained question to a local LLM and receive an instant answer. Ideal for summarising, extracting, reformatting, classifying, or drafting text you already have.

Instructions

Ask Yevgeny (the local model) a single self-contained question and get the answer back immediately. No tools, no web access, one turn. Use for summarising, extracting or reformatting text you already have, classifying items, or drafting. For anything needing research, files or computation use delegate_start instead.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
filesNoAbsolute paths to read and prepend as context
modelNoOllama model, default gemma4:12b
promptYesThe full question or instruction, including any text to work on
Install Server

TDQS

A3.9/5.0
Behavior2/5

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

With no annotations, the description must carry the full burden. It is transparent about being one-turn and offline, but it tells agents to use delegate_start for anything needing files, while the input schema offers a files parameter for reading absolute paths as context. This internally contradicts the tool's actual capabilities.

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?

Two tight sentences: the first defines the core behavior, the second scopes use cases and exclusions. Key constraints are front-loaded with no filler.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness3/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

The description covers the main use cases, exclusions, and the one-tun nature, and schema descriptions cover all parameters. However, the conflict around file handling prevents an agent from confidently deciding whether the files parameter should be used or avoided.

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%, so prompt, model and files are already documented. The description adds only that the prompt should be a single self-contained question, but it muddies the files parameter by delegating file-related requests elsewhere.

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?

Description names a specific verb and resource: asking Yevgeny a single self-contained question and getting an immediate one-turn answer. It also distinguishes itself from the delegate_* siblings by stating 'No tools, no web access, one turn.'

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?

Gives explicit usage context: summarising, extracting, reformatting, classifying, drafting. It also states when not to use it — for research, files or computation use delegate_start — so an agent has clear routing criteria.

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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curl -X GET 'https://glama.ai/api/mcp/v1/servers/pSuarezFrancisco/yevgeny-mcp'

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