obsidian-modified-mcp-server

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semantic_search

Search Obsidian notes by meaning, not just keywords. Enter a natural language query to find conceptually related content based on context similarity.

Instructions

Concept-based search via Smart Connections. Finds conceptually related content using meaning/context similarity rather than keyword matching.

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": "semantic_search"
  }
}

Input Schema

Name	Required	Description
`limit`	No	Maximum results to return (default: 10).
`query`	Yes	Natural language concept query.
`filters`	No
`vaultId`	No	Optional vault ID (defaults to configured default vault).
`threshold`	No	Similarity threshold 0-1 (default: 0.7). Higher = more precise.

Schema Changelog

Changes observed during successful MCP inspections.

First observedv0.6.0Jul 31, 2026

TDQS

B3.2/5.0

Behavior2/5

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

Despite having no annotations, the description only covers the conceptual approach and does not disclose result format, behavior around threshold/filters, or any side effects. This leaves the agent without critical behavioral context for a search operation.

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 two short sentences, immediately front-loading the core concept ('Concept-based search via Smart Connections') with no redundant filler.

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

Completeness2/5

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

There is no output schema, and the tool has a fairly complex input schema with nested filters and a threshold parameter. The description does not mention these features, return behavior, or how to distinguish this tool from many search-sibling tools, making it incomplete for effective use.

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

Parameters3/5

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

Schema description coverage is 80% (4 of 5 parameters documented), so the baseline is 3. The description adds no additional parameter semantics, such as how threshold or filters affect the search, so it does not exceed the baseline.

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 clearly states 'Concept-based search via Smart Connections' and contrasts with 'keyword matching,' giving a specific verb+resource+scope. However, it does not explicitly differentiate from sibling semantic search tools like find_similar_notes, so it falls short of a 5.

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 phrase 'rather than keyword matching' implicitly suggests using this tool for conceptual/semantic queries and others for literal matches, but no explicit when-to-use/when-not-to-use guidance or alternative tool names are provided.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.