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context_relevance_score

Calculate a RankRAG-style relevance score for a moment against a task context, using an optional semantic query to refine relevance weighting for focused context curation.

Instructions

Calculate RankRAG-style relevance score for a moment given a task context

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": {
      "moment_id": "<moment_id>",
      "task_id": "<task_id>"
    },
    "name": "context_relevance_score"
  }
}

Input Schema

Name	Required	Description
`query`	No	Optional semantic query for additional relevance weighting
`task_id`	Yes	Task context for relevance calculation
`moment_id`	Yes	Moment to score

Schema Changelog

Changes observed during successful MCP inspections.

First observedv0.4.0Aug 1, 2026

TDQS

C2.9/5.0

Behavior2/5

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

There are no annotations, so the description carries the full burden of behavioral disclosure. It states 'Calculate' but does not explain score range, meaning, side effects, read-only behavior, or the RankRAG-style algorithm referenced, leaving significant behavioral ambiguity.

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, fluff-free sentence. It is efficient, though extremely terse, lacking contextual depth that could be added without becoming verbose.

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?

The tool has no output schema and no annotations, but the description does not explain the return format or what the relevance score represents. For a scoring tool, this is a significant gap, making the description incomplete for an agent to anticipate results.

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 coverage is 100%, with each parameter described in the schema, including the optional query. The description adds no parameter-specific meaning beyond what the schema already provides, so baseline 3 is appropriate.

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 clearly states the tool calculates a relevance score for a moment given a task context, using a specific verb and resource. It is specific enough to be distinct from export/list tools, but it does not explicitly differentiate itself from similar analytical siblings like evaluate_context_window or predict_agent_needs.

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?

No guidance is provided on when to use this tool versus alternatives. The description only implies usage when a moment and task context are available, but does not state context, preconditions, or exclusions relative to sibling tools.

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