Skip to main content
Glama
sinner12123

mcp-glm-kimi-ollama

by sinner12123

kimi_think

Solves complex problems by generating extended step-by-step reasoning, breaking them into logical steps with optional context.

Instructions

Extended step-by-step reasoning for complex problems.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
contextNoAdditional context
problemYesProblem or question to reason about
workFolderNo
Behavior3/5

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

With no annotations available, the description carries the burden of disclosing behavior. It does reveal that the tool performs extended, step-by-step reasoning, which is a key behavioral trait. However, it does not disclose what the output looks like, whether there are side effects, or how the context and workFolder parameters influence the reasoning.

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 a single, front-loaded sentence with no filler. Every phrase adds meaning: 'Extended step-by-step reasoning' specifies the nature, and 'for complex problems' identifies the use case. This is appropriately concise.

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

Completeness2/5

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

There is no output schema, so the description should explain what the tool returns, but it only mentions the reasoning process. It also lacks guidance on how context and workFolder should be used and does not distinguish when to prefer this tool over overlapping siblings like kimi_agent or kimi_research. This leaves important gaps for an agent deciding how to invoke the tool correctly.

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 provides descriptions for two of the three parameters (problem and context), while workFolder lacks a description. The tool description itself adds no parameter-specific meaning, so it does not enhance the schema's coverage. At 67% coverage, the description is adequate but does not compensate for the undocumented workFolder parameter.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose4/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description states a clear function: 'Extended step-by-step reasoning for complex problems.' It names the target input (complex problems) and the behavior (extended step-by-step reasoning). It does not explicitly distinguish from siblings like kimi_agent or kimi_research, so it falls short of a 5.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines3/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The phrase 'for complex problems' implies when the tool should be used, giving some context. However, it does not provide explicit guidance on when not to use it or mention alternatives among the sibling tools, so usage guidance is only implied rather than fully articulated.

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

Install Server

Other Tools

Latest Blog Posts

MCP directory API

We provide all the information about MCP servers via our MCP API.

curl -X GET 'https://glama.ai/api/mcp/v1/servers/sinner12123/mcp-glm-kimi-ollama'

If you have feedback or need assistance with the MCP directory API, please join our Discord server