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minuetai
by minuetai

get_network_stats

Retrieve aggregate statistics from the Minuet relationship graph, including total relationships, agents, named agents, verification status, and top relationship types.

Instructions

Return aggregate statistics about the Minuet relationship graph.

Returns totals for relationships, agents, named agents, verified vs inferred status, and the top relationship types.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Implementation Reference

  • The actual tool handler: decorated with @mcp.tool(), fetches clusters and relationships from the Minuet API, computes status and relationship_type counters, merges with cluster stats, and returns the aggregate dictionary.
    @mcp.tool()
    async def get_network_stats() -> dict[str, Any]:
        """Return aggregate statistics about the Minuet relationship graph.
    
        Returns totals for relationships, agents, named agents, verified vs
        inferred status, and the top relationship types.
        """
        from collections import Counter
    
        async with MinuetClient() as client:
            clusters = await client.get_clusters()
            rels = await client.list_relationships()
    
        relationships = rels.get("relationships", [])
        status_counts: Counter[str] = Counter(r.get("status", "") for r in relationships)
        type_counts: Counter[str] = Counter(
            r.get("relationship_type", "") for r in relationships
        )
    
        stats = dict(clusters.get("stats", {}))
        stats["by_status"] = dict(status_counts)
        stats["by_relationship_type"] = dict(type_counts)
        return stats
  • Registration via the @mcp.tool() decorator on the get_network_stats function.
    @mcp.tool()
  • Helper API methods list_relationships() and get_clusters() used inside the handler to fetch data from the Minuet API.
    async def list_relationships(self) -> dict[str, Any]:
        return await self._get("/api/relationships")
    
    async def get_clusters(self) -> dict[str, Any]:
        return await self._get("/api/relationships/clusters")
  • Test verifying get_network_stats is registered among expected tool names.
    "get_network_stats",
  • Test expected tools set including get_network_stats for STDIO protocol test.
    "get_network_stats",
Behavior4/5

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

No annotations provided, but the description honestly discloses that it returns aggregate stats, not individual data, and lists the specific statistics. No hidden behaviors or contradictions.

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: first states purpose and key resource, second lists details. Front-loaded, no wasted words.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness5/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

For a parameterless tool with no annotations and an output schema (implied by context), the description fully covers what the tool does and what it returns. No gaps.

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

Parameters4/5

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

Input schema has no parameters (0), so schema coverage is 100%. Description adds meaning by specifying the exact statistics returned, going beyond what an empty schema provides.

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?

Clearly states it returns aggregate statistics about the Minuet relationship graph and lists specific items (totals for relationships, agents, named agents, verified vs inferred status, top relationship types). Distinct from siblings like get_agent (specific agent) and get_relationship_graph (graph structure).

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?

Implied usage is when overall network statistics are needed. No explicit when-not-to-use or alternatives, but siblings are sufficiently different that the tool's role is clear.

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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