Skip to main content
Glama
cog-sh
by cog-sh

semantic_search

Search an Obsidian vault by hybrid semantic and keyword relevance, returning cited chunks with file paths to ground answers and locate relevant notes.

Instructions

Hybrid (dense+BM25, RRF) semantic search over the Obsidian vault. Returns chunks with file_path citations.

chunks_per_note raises how many chunks one long note may contribute (default 1, the previous behaviour). Every result carries index_stale: how many notes changed since the last index — 0 means the results are current, -1 means the index has never run. Trust accordingly.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
kNo
tagNo
queryYes
folderNo
chunks_per_noteNo

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.1.0

TDQS

B3.2/5.0
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 it does well: it discloses the retrieval method (dense+BM25 fused with RRF), the return content (chunks with citations), and, notably, the semantics of the `index_stale` result field (0 = current, -1 = never run) so the agent can gauge trust. It stops short of 5 only because it omits things like result freshness implications of running a search against a stale index or whether an indexing step is required first.

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 the core capability, then a short paragraph on the one non-obvious parameter and the staleness signal. Every sentence earns its place; minor verbosity in the parenthetical '(default 1, the previous behaviour)'.

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

Completeness3/5

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 explained, yet the description usefully clarifies the subtle `index_stale` field. However, for a 5-parameter tool with 0% schema coverage it leaves `k`, `tag`, and `folder` entirely unexplained, which is a real gap for correct invocation.

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

Parameters2/5

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

Schema description coverage is 0% across 5 parameters. The description explains `chunks_per_note` well and implies `query`, but `k`, `tag`, and `folder` receive no meaning, syntax, or filtering guidance in either the schema or the description, leaving half the surface undocumented.

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?

Names a specific verb+resource: hybrid (dense+BM25, RRF) semantic search over the Obsidian vault, and states the return shape (chunks with file_path citations). It does not explicitly differentiate itself from near siblings like find_related or list_notes, so it stops 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 Guidelines2/5

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

There is no when-to-use guidance and no routing to alternatives such as find_related, read_note, or a keyword search. The reader must infer that this is the tool for semantic retrieval over the vault, and nothing tells them when a different sibling is preferable.

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