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muninn-local-mcp

muninn-local-mcp

by wolfcao
OverviewSchemaSourceRelated ServersDiscussionsAdmin
Python
Local

memory_search

Search project memory using semantic similarity to find relevant stored information. Provide a query and optionally limit results.

Instructions

Search the current project's memory store using semantic similarity.

Embeds the query and returns the most relevant stored memories.

Args: query: The search query text (required). top_k: Maximum number of results to return (default 5).

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": {
      "query": "<query>"
    },
    "name": "memory_search"
  }
}

Input Schema

NameRequiredDescriptionDefault
queryYes
top_kNo

Output Schema

NameRequiredDescriptionDefault
resultYes

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.1.0

TDQS

A4.3/5.0
Behavior3/5

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

No annotations are provided, so the description carries full burden. It states the tool performs semantic similarity search and returns relevant memories, but does not disclose any behavioral traits such as read-only nature, auth needs, or edge cases. Adequate but not detailed.

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, with a clear first sentence stating purpose, followed by a brief implementation note, and an ordered list of parameters. No unnecessary information.

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

Completeness4/5

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

For a simple search tool with two parameters and an output schema, the description covers purpose, parameters, and method. It does not detail return values, but with output schema present, that is acceptable. Minor gap in behavioral transparency prevents a 5.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters5/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

The Args section in the description clearly explains both parameters: 'query' as required search text and 'top_k' as maximum results with a default of 5. This adds meaning beyond the schema, which only defines types and default.

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 that the tool searches the current project's memory store using semantic similarity, distinguishing it from global memory search tools. It specifies the verb 'search' and the resource 'current project's memory store'.

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?

The description implies local scope ('current project') versus global siblings, but does not explicitly state when to use this tool over alternatives like global_memory_search. It provides clear context but lacks 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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