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dakera-mcp

dakera-mcp

Official
by Dakera-AI
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Rust
Remote

dakera_knowledge_graph

Construct a knowledge graph from a seed memory using embedding similarity to discover how a concept links to stored knowledge.

Instructions

Build a knowledge graph from a seed memory using embedding similarity. Use to explore how a concept connects to stored knowledge. For BFS traversal of an existing linked graph use dakera_graph_traverse.

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": {
      "agent_id": "<agent_id>",
      "memory_id": "<memory_id>"
    },
    "name": "dakera_knowledge_graph"
  }
}

Input Schema

NameRequiredDescriptionDefault
depthNoGraph traversal depth (controls candidate count)
agent_idYes
memory_idYesSeed memory ID to build graph from
min_similarityNoMinimum similarity threshold 0.0-1.0

Schema Changelog

Changes observed during successful MCP inspections.

  1. Addedv0.10.12
  2. Removedv0.10.11
  3. Addedv0.10.8

TDQS

A3.8/5.0
Behavior2/5

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

With no annotations to rely on, the description carries the full burden of disclosing behavioral traits. However, it only states the action ('build a knowledge graph') without clarifying whether this is a read-only operation, what side effects exist, or what the return structure looks like. The term 'build' could imply a mutation, but this is left ambiguous.

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 two sentences long, with no redundant content. It front-loads the primary function, then adds the use case and the differentiation from the sibling tool. Every word contributes to clarity.

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?

No output schema is provided, so the description should hint at what the tool returns. It does not mention the result format (e.g., nodes, edges, graph object) nor any error or edge-case behavior. Given the complexity of building a knowledge graph, the description is insufficient for an agent to fully anticipate the tool's output.

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?

The schema covers 75% of parameters with descriptions, but the tool description adds no additional insight about any of them. The missing description for agent_id is not compensated for by the prose. The description does not clarify acceptable values, defaults, or interactions between parameters, leaving the semantics solely to the 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 clearly states the tool's function (build a knowledge graph using embedding similarity), its intended use case (explore concept connections), and explicitly differentiates it from a sibling tool (dakera_graph_traverse) for BFS traversal of existing graphs.

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?

It provides explicit guidance on when to use this tool ('Use to explore how a concept connects to stored knowledge') and when to use an alternative ('For BFS traversal of an existing linked graph use dakera_graph_traverse'). No ambiguity remains.

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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