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epsilongraph

epsilongraph

by silverenternal
OverviewSchemaSourceRelated ServersDiscussionsAdmin
Python
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vault_search

Search an Obsidian knowledge graph for ranked notes matching a query, returning match explanations and provenance so agents can ground claims and trace evidence.

Instructions

Ranked notes for a query, with match explanation and provenance.

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": "vault_search"
  }
}

Input Schema

NameRequiredDescriptionDefault
kNo
layerNoe.g. Concepts/
queryYes
statusNo
grounded_onlyNo

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.1.0

TDQS

C2.6/5.0
Behavior2/5

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

With no annotations, the description carries the full burden of behavioral disclosure. It mentions output features (match explanation, provenance) but does not state that the operation is read-only, whether it has side effects, or any permission requirements, leaving important behavior unspecified.

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?

The description is a single, front-loaded sentence with no wasted words. It is appropriately concise, though its brevity contributes to gaps in other dimensions rather than being a structural flaw.

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?

Given five parameters, low schema coverage, no annotations, and no output schema, the description is incomplete. It omits parameter explanations, usage context, and sufficient return detail to fully guide correct invocation.

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

Parameters1/5

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

Schema description coverage is only 20%, and the description does not explain any of the five parameters. It does not clarify what k, layer, status, or grounded_only do, nor does it add meaning beyond the parameter names.

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 states a specific resource (notes) and the operation (ranked retrieval for a query), plus output features (match explanation, provenance). It is clear enough to distinguish from most siblings, but it does not explicitly differentiate from vault_get or vault_neighbors, keeping it at 4 rather than 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 guidance on when to use this tool versus alternatives like vault_get or vault_neighbors. The description only says what it returns, not the context or conditions under which it should be chosen.

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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