LifeOS MCP Server

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lifeos_search

Search indexed vault files semantically to find relevant passages with source, heading, and similarity scores. Filter by category or set top_k for targeted results.

Instructions

Perform semantic vector search across indexed vault files.

Returns relevant text passages with explicit provenance: [Source: | Heading: | Similarity: ].

Args: query: Natural language query to search for. category: Optional vault category filter (e.g. 'projects', 'goals', 'health', 'learning'). top_k: Number of relevant chunks 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": "lifeos_search"
  }
}

Input Schema

Name	Required	Description	Default
`query`	Yes
`top_k`	No
`category`	No

Output Schema

Name	Required	Description	Default
`result`	Yes

Schema Changelog

Changes observed during successful MCP inspections.

First observedv0.1.0Oct 4, 2026

TDQS

A3.6/5.0

Behavior3/5

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

No annotations are provided, so the description carries the burden. It does disclose the return shape concretely via the provenance template '[Source: <path> | Heading: <heading> | Similarity: <score>]', which is genuine behavioral context. However it says nothing about indexing state, latency, permissions, or result limits, leaving real gaps for a search 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?

Front-loaded with purpose, then return format, then a compact Args list. Every line earns its place and the reader can stop after the first sentence if only the purpose matters. The bracketed format string is slightly dense but functional.

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?

An output schema exists, so return values need not be re-explained, and the description still adds the provenance format. Purpose, all three parameters, and the result shape are covered; only the usage routing against siblings is thin.

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 description coverage is 0%, so the description must compensate — and it does, documenting all three parameters: query as natural language, category as an optional filter with concrete example values, and top_k with its default. Only the enumerated category values remain unspecified, keeping this just short of a 5.

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 specific verb and resource: 'semantic vector search across indexed vault files', which immediately distinguishes it from a literal file read or a memory write. It is clear but never names a sibling (e.g. lifeos_read_file) as an alternative, so the differentiation is left to inference.

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?

Usage is only implied by 'semantic vector search' — an agent can infer this is for finding relevant passages by meaning rather than exact path lookup, but there is no explicit when-to-use, when-not-to-use, or named alternative among the siblings. No prerequisites or conditions are stated.

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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