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Michael-WhiteCapData

ollama-handoff

by Michael-WhiteCapData
OverviewSchemaSourceRelated ServersDiscussionsAdmin
Python
Local

chat_local

Conduct multi-turn chats with a local Ollama model, preserving full conversation context for handoffs that need more than one turn. Runs locally at no cloud cost.

Instructions

Hold a multi-turn chat against a local Ollama model.

Use instead of ask_local when the handoff needs more than one turn of context — a running conversation or a system + user + assistant history. Runs locally at no cloud cost. Returns the model's next assistant message as text.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
modelNoOllama model name to run, e.g. 'llama3.1' or 'qwen2.5-coder'. Omit to use the server's configured default model.
messagesYesConversation as a list of {"role": "user"|"assistant"|"system", "content": str} messages, in order.

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes

Schema Changelog

Changes observed during successful MCP inspections.

  1. Changed2 schema fields changedv0.1.3
    • addedInput schema / properties / messages / description
      Added value: +"Conversation as a list of {\"role\": \"user\"|\"assistant\"|\"system\", \"content\": str} messages, in order."
    • addedInput schema / properties / model / description
      Added value: +"Ollama model name to run, e.g. 'llama3.1' or 'qwen2.5-coder'. Omit to use the server's configured default model."
  2. First observedv0.1.0

TDQS

A4.5/5.0
Behavior4/5

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

No annotations are provided, so the description carries the full burden of behavioral disclosure. It discloses that the tool runs locally at no cloud cost and returns the next assistant message as text, which are useful behavioral traits. It does not explicitly mention potential failure modes (e.g., model unavailability, timeouts) or confirm it is read-only, but these are less critical for a chat tool.

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 concise and well-structured. The first paragraph states the purpose and usage guidance, and the second adds contextual details. Every sentence contributes value without redundancy or fluff.

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 that an output schema exists, the description does not need to explain the return format in depth. It covers the tool's purpose, when to use it versus an alternative, and the basic return type, making it complete for an agent to correctly invoke the tool.

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?

The input schema already documents both parameters (model and messages) with detailed descriptions, achieving 100% coverage. The tool description does not add additional parameter-level semantics, but the schema is sufficient, so the baseline score of 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 states the tool's specific function: holding a multi-turn chat against a local Ollama model. It clearly distinguishes from the sibling `ask_local` by the multi-turn requirement, so an agent can immediately understand its purpose and differentiate it from related tools.

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 says 'Use instead of `ask_local` when the handoff needs more than one turn of context' and provides concrete examples ('a running conversation or a system + user + assistant history'), leaving no ambiguity about when to select this tool over its alternative.

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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