personal-kg-mcp

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kg_get_relevant_context

Retrieve relevant past decisions, insights, and open questions to inject context before starting work.

Instructions

Retrieves relevant past context for a given query. Returns brief summaries of related decisions, insights, and open questions. Perfect for proactive context injection before starting work on a topic.

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": "kg_get_relevant_context"
  }
}

Input Schema

Name	Required	Description
`query`	Yes	The topic or task to find relevant context for
`project`	No	Optional project name to scope the search (normalized to 'proj:project-name')
`max_items`	No	Maximum number of context items to return
`include_questions`	No	Whether to include open questions in the context

Schema Changelog

Changes observed during successful MCP inspections.

First observedv1.2.0Jul 31, 2026

TDQS

A4/5.0

Behavior3/5

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

No annotations are provided, so the description bears full responsibility for behavioral disclosure. It accurately indicates a read-only retrieval action and describes the nature of returned data, but does not explicitly state side-effect-free behavior, authentication needs, or how relevance is determined. This is adequate but not richly transparent.

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?

Two sentences with zero filler. The first sentence states the core function, the second adds practical usage context. Information is front-loaded and every word earns its place.

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 moderate-complexity tool with full parameter documentation and no output schema, the description covers the what, the output nature, and the when. It is complete enough for an agent to select and invoke correctly, though a note on return format or empty results would fully round it out.

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 description coverage is 100%, so the baseline is 3. The description does not add any parameter-level detail beyond the schema, but none is needed since all parameters are already well-documented in the input schema.

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 opens with a specific verb and resource: 'Retrieves relevant past context for a given query.' It further clarifies the output content (summaries of decisions, insights, open questions), distinguishing it from sibling tools like kg_search or kg_get_project_state by highlighting proactive context injection.

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?

Clearly states the intended use case: 'Perfect for proactive context injection before starting work on a topic.' This gives explicit context for when to use the tool, though it does not name alternative tools or exclusion cases, which would elevate it to a 5.

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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