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lore_search

Search local markdown notes for what they say about a topic, returning the best passage per note with source file, match score, and linked entities. Supports multi-hop associations.

Instructions

Hybrid retrieval over the markdown vault: BM25 + knowledge-graph spreading activation (+ dense embeddings when configured). Returns one passage per note — the section that best covers your query — with the file it came from, how much of your query it matched, and any entity that linked it in. Use for any "what do my notes say about X" question, including multi-hop associations where the answer shares no words with the query. Pass verbose:true only if you need score internals.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
kNomax results (default 8)
tagsNoonly notes carrying every listed tag; prefix a tag with "-" to exclude it
queryYesnatural-language query
sinceNoonly content dated on/after this ISO date
untilNoonly content dated on/before this ISO date
folderNoonly notes under this vault-relative folder, e.g. "projects/"
verboseNoinclude score breakdown and block anchors

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.38.0

TDQS

A3.9/5.0
Behavior4/5

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

With no annotations, the description carries the full burden and does well: it discloses the retrieval mechanism, that it returns one passage per note (the best-matching section), and what metadata accompanies it. It omits auth/permission needs and performance characteristics, keeping it out of 5 territory.

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?

Three tight sentences with the mechanism front-loaded, then usage, then the param caveat. Dense but every clause carries information; minor cost from the parenthetical technical detail.

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 search tool with no output schema and no annotations, the description tells the agent what it gets back (passage, source file, match proportion, linking entity) and when to escalate to verbose. Missing only operational details like auth or result limits.

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 coverage is 100%, so the schema documents all seven parameters; baseline is 3. The only added meaning is the verbose hint ("only if you need score internals"), which is a mild addition over the schema's description of score breakdown.

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?

States a specific verb and resource (hybrid retrieval over the markdown vault) and names the exact retrieval methods (BM25 + knowledge-graph spreading activation + dense embeddings). It also defines the return shape precisely. It does not explicitly name a sibling it differs from, 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 Guidelines4/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

Gives an explicit when-to-use trigger ("any 'what do my notes say about X' question") and even the harder case (multi-hop associations sharing no words with the query). It adds a conditional param rule for verbose, but names no alternative sibling or when-not-to-use condition.

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