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selvin-search-mcp

selvin-search-mcp

by selvinchen97-lab
OverviewSchemaSourceRelated ServersDiscussionsAdmin
Python
Local

switch_model

Switch the AI model used for search summarization, persisting the selection for future requests.

Instructions

Switches the GLM-compatible model used for search summarization, persisting the setting.

**Key Features:**
    - **Model Selection:** Change the AI model for web search and content fetching.
    - **Persistent Storage:** Model preference saved to ~/.config/selvin-search/config.json.
    - **Immediate Effect:** New model used for all subsequent operations.

**Edge Cases & Best Practices:**
    - Use get_config_info to verify available models before switching.
    - Invalid model IDs may cause API errors in subsequent requests.
    - Model changes persist across sessions until explicitly changed again.

Tool call template

Replace each placeholder with a value matching the input schema. Only required parameters are included; add optional parameters as needed.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {
      "model": "<model>"
    },
    "name": "switch_model"
  }
}

Input Schema

NameRequiredDescriptionDefault
modelYesModel ID to switch to.

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.1.0

TDQS

A4.8/5.0
Behavior5/5

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

With no annotations, the description fully discloses behavior: the change takes immediate effect, persists to ~/.config/selvin-search/config.json, and invalid model IDs may cause API errors in subsequent requests. This is complete transparency for a mutation tool.

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 well-structured with 'Key Features' and 'Edge Cases' sections, front-loading the core purpose. It is slightly verbose with some redundancy (e.g., 'Model Selection' and 'Immediate Effect'), but every sentence adds value.

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?

For a simple one-parameter tool with no output schema, the description covers all necessary context: what the tool does, how it persists, prerequisites (model ID from get_config_info), and consequences of invalid input. It is self-sufficient.

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% with a clear description for the 'model' parameter. The description adds value by linking to get_config_info for valid IDs and explaining the context (search summarization), going beyond the schema alone.

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 the tool's purpose: switching the GLM-compatible model used for search summarization and persisting the setting. It differentiates from siblings by specifying the domain (search summarization) and persistent configuration, distinguishing it from informational tools like get_config_info.

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?

Explicit best practices are provided: use get_config_info to verify available models before switching, warning about invalid model IDs causing subsequent errors, and noting persistence across sessions. This gives clear guidance on when and how to use the tool.

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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