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pvliesdonk

markdown-vault-mcp

by pvliesdonk

Similar Notes

get_similar
Read-onlyIdempotent

Retrieve notes semantically similar to a reference document using precomputed embeddings. Excludes the reference from results and requires semantic search setup.

Instructions

Find notes most semantically similar to the given document.

Uses stored embedding vectors — no re-embedding needed. The reference document is excluded from results. Requires semantic search to be configured (check 'stats' for semantic_search_available). Returns an empty list if embeddings are not configured (check 'embeddings_status') or the document has no stored vectors (call 'build_embeddings' to embed missing chunks).

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
pathYesRelative path of the reference document (e.g. "notes/topic.md"). Case-sensitive.
limitNoMaximum number of similar notes to return (default 10).
chunks_per_fileNoMaximum sections returned per file (default 2). Set to 1 for one best section per file. Must be >= 1.
wait_for_pending_writesNoWhen True, wait until your recent write/edit/delete/rename operations have been applied to the index before answering, so the results reflect those changes. Use it right after modifying notes when this read must see them (e.g. right after a write/edit/delete/rename whose effect this read should reflect). Default False answers immediately from the current index — almost always already up to date; inspect the response's ``_meta.index_stale`` field to tell whether a write was still in flight. Bounded by a server timeout (default 60s); on timeout it answers from the current index rather than waiting longer.

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes
Behavior5/5

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

The description discloses key behavioral traits beyond annotations: uses stored embeddings (no re-embedding), excludes reference document, requires configuration, and returns empty list in specific edge cases. This adds substantial context to the readOnlyHint and idempotentHint annotations.

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 concise and well-structured: first sentence states purpose, followed by succinct bullet-like sentences covering behaviors, prerequisites, and edge cases. Every sentence adds value with no redundancy, earning its place.

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

Completeness5/5

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

Given the complexity of semantic search and embeddings, the description covers all necessary aspects: what it does, prerequisites, edge cases, and the wait behavior. The presence of an output schema means return values are covered, so the description is fully complete.

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?

While schema coverage is 100% and baseline is 3, the description adds meaning by explaining the system context for parameters like 'wait_for_pending_writes' (index eventual consistency) and the overall reliance on embeddings configuration, which helps agents understand parameter purpose beyond individual schema descriptions.

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 explicitly states the tool's purpose with a specific verb ('Find notes') and resource ('most semantically similar to the given document'), clearly distinguishing it from siblings like 'search' (keyword-based) and 'get_most_linked' (link-based).

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines5/5

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

The description provides explicit when-to-use guidance and prerequisites ('requires semantic search to be configured'), alternative actions ('call 'build_embeddings' to embed missing chunks'), and notes that the reference document is excluded from results, giving clear context for appropriate usage.

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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