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muninn-local-mcp

muninn-local-mcp

by wolfcao
OverviewSchemaSourceRelated ServersDiscussionsAdmin
Python
Local

global_memory_write

Write a memory entry to a global store shared across all projects. Save text with optional type and tags for cross-project retrieval.

Instructions

Write a memory entry to the global (cross-project) memory store.

Global memories are shared across all projects and can be queried regardless of which project the MCP server is running in.

Args: text: The memory content to store (required). memory_type: Type of memory. Must be one of: summary, decision, next-steps, code-pattern, note (default). tags: Comma-separated tags for categorization (optional).

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": {
      "text": "<text>"
    },
    "name": "global_memory_write"
  }
}

Input Schema

NameRequiredDescriptionDefault
tagsNo
textYes
memory_typeNonote

Output Schema

NameRequiredDescriptionDefault
resultYes

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.1.0

TDQS

A3.8/5.0
Behavior2/5

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

No annotations are provided, and the description does not disclose behavioral traits such as idempotency, overwrite behavior, size limits, or auth requirements. The description is functional but lacks depth.

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 a clear main purpose and bullet points for parameters. It is slightly verbose but efficient overall.

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?

Given that an output schema exists, return values are handled. The description covers param semantics and purpose well but lacks behavioral transparency, limiting completeness for a mutation tool.

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?

The description covers all three parameters: text (required, content), memory_type (allowed values with default), and tags (optional, comma-separated). However, there is a mismatch: schema lacks enum enforcement, but description lists values.

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 writes a memory entry to the global memory store, specifying it is cross-project. This differentiates it from per-project memory tools.

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?

It explains that global memories are shared across projects, implying when to use this vs memory_write. However, it does not explicitly list alternatives or exclusions.

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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