Retrieves context from Obsidian vaults and documents to answer questions via LLM, supporting source and path filters, similarity thresholds, and top-k selection.
Retrieve context and answer the question with the LLM.
| Name | Required | Description | Default |
|---|---|---|---|
| top_k | No | ||
| source | No | ||
| question | Yes | ||
| path_prefix | No | ||
| score_threshold | No |
Changes observed during successful MCP inspections.
v0.1.0Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are present, so the description carries full responsibility for behavioral disclosure. It states that the tool retrieves context and answers with an LLM, but it does not say whether it is read-only, whether it requires auth or configuration, how sources are selected, or what the response contains.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The text is a single short, declarative sentence with no filler, so it is structurally concise. It is under-specified for a five-parameter tool, but the wording that exists is front-loaded and not redundant.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
With five parameters, no output schema, and no annotations, this description is far too thin to support correct invocation. An agent learns that the tool answers with an LLM, but not how the question, retrieval filters, or scoring thresholds behave or what the returned answer looks like.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 0%, and the description compensates for none of the five parameters. Only 'question' is implied by 'answer the question'; top_k, source, path_prefix, and score_threshold are left completely unexplained.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description uses a specific verb ('retrieve context and answer') and identifies the LLM as part of the behavior, so an agent can tell this is a question-answering tool rather than a raw listing tool. It does not, however, distinguish it from the sibling 'search' tool or state what context is being retrieved.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides no guidance about when to prefer 'query' over siblings such as 'search', 'list', or 'stats', and it offers no exclusions or alternatives. An agent is left to infer the intended use from the parameter names.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.