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precedent

precedent

by ZNLong2203
OverviewSchemaSourceRelated ServersDiscussionsAdmin
Python
Local

graph_schema

Read-onlyIdempotent

Retrieve the graph model and example Cypher queries for querying precedent decisions, so coding agents can build or adjust graph queries without guessing schema relationships.

Instructions

The graph model and example Cypher for query_graph.

Tool call template

This tool declares no required parameters. Add optional parameters from the input schema as needed.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {},
    "name": "graph_schema"
  }
}

Input Schema

NameRequiredDescriptionDefault

No arguments

Output Schema

NameRequiredDescriptionDefault

No arguments

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.1.0

TDQS

A3.6/5.0
Behavior3/5

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

Annotations already declare readOnlyHint, idempotentHint, destructiveHint=false and openWorldHint=false, so the safety profile is covered. The description adds only that the payload is a graph model plus example Cypher — useful content context, but nothing about size, freshness or whether examples are exhaustive of the graph's relationships.

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?

One short sentence with no filler, and the key noun (graph model / example Cypher) is front-loaded. It is efficient, though so compressed that the sentence reads more like a caption than a definition.

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?

A zero-parameter, read-only lookup with an output schema and full annotation coverage needs little prose, and the description correctly points at the sibling it supports. The remaining gap is guidance on when to invoke it, which no structured field supplies.

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?

The tool takes zero parameters, so there is nothing for the description to disambiguate; the baseline of 4 applies. No parameter meaning is lost by the terse text.

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?

Names a specific resource (the graph model plus example Cypher) and ties it to the sibling `query_graph`, so an agent can tell it is a reference/schema-lookup tool rather than a query executor. It stops short of saying outright that it describes the graph's nodes/edges, but the verb-plus-resource intent is legible.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines3/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The link to `query_graph` implies this should be consulted when composing Cypher, but the description never states when to call it (e.g. before a first query or when a query fails) or what it is not for. Usage is inferred rather than instructed.

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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