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muninn-local-mcp

muninn-local-mcp

by wolfcao
OverviewSchemaSourceRelated ServersDiscussionsAdmin
Python
Local

global_memory_search

Retrieves semantically similar memories from all projects in the global memory store. Enter a query to find relevant stored information across your entire workspace.

Instructions

Search the global (cross-project) memory store using semantic similarity.

Embeds the query and returns the most relevant stored memories from all projects.

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": "global_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

A4/5.0
Behavior3/5

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

No annotations provided, so description bears full burden. It discloses semantic similarity and result retrieval but lacks details on embedding model, pagination, max top_k limits, or handling of no results.

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?

Description is concise with an Args section, no extraneous content, and front-loaded purpose. Each sentence serves a purpose.

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?

Given output schema exists, description needn't detail return format. It covers purpose and parameters adequately for a search tool, though could mention memory content scope.

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 0%, so description compensates. It explains 'query' as required search text and 'top_k' as max results with default 5, adding meaning beyond schema titles.

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 searches the global memory store using semantic similarity, specifying the verb (search), resource (global memory), and method (semantic similarity). This distinguishes it from sibling tools like global_memory_write and memory_search.

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 description implies usage for cross-project search but does not explicitly state when to use vs. alternatives like memory_search (project-specific). No exclusions or when-not guidance provided.

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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