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DPC MCP Server

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by Dans-Plugins

graphql

Run read-only GraphQL queries to explore note connections, clusters, and repository grounding. Get answers on relationships and citations across the knowledge base.

Instructions

Run a GraphQL query against the collection's structure. Use this for questions the other tools cannot answer — which notes are most connected, what a cluster contains, which repositories ground the most claims, how two notes relate.

Read-only: no mutations, fragments, or variables. Inline argument values.

Examples: { notes(orderBy: degree, first: 5) { title degree moc { title } } } { notes(moc: "moc-faction-domain-model") { title summary } } { note(id: "demesne-limit") { title links { title } sources { claim url } } } { repositories { name citationCount noteCount } }

SCHEMA:

The zettelkasten as a graph. Read-only — queries only.

enum NoteType { concept moc } enum NoteOrder { title degree citations }

type Query { notes(type: NoteType, moc: ID, tag: String, repo: String, search: String, linkedTo: ID, orderBy: NoteOrder, first: Int): [Note!]! note(id: ID!): Note mocs: [Note!]! concepts(first: Int): [Note!]! tags: [Tag!]! repositories: [Repository!]! stats: Stats }

type Note { id: ID title: String type: NoteType summary: String tags: [String!]! updated: String path: String url: String moc: Note links(first: Int): [Note!]! backlinks(first: Int): [Note!]! neighbors(first: Int): [Note!]! linkCount: Int backlinkCount: Int degree: Int sources: [Source!]! sourceCount: Int repositories: [String!]! }

type Source { repo: String path: String ref: String shortRef: String lines: String claim: String url: String }

type Tag { name: String count: Int notes: [Note!]! }

type Repository { name: String url: String citationCount: Int noteCount: Int pinnedRefs: [String!]! notes: [Note!]! }

type Stats { noteCount: Int mocCount: Int conceptCount: Int citationCount: Int linkCount: Int repositoryCount: Int repositories: [String!]! updated: String }

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
queryYesThe GraphQL query document.
Behavior5/5

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

With no annotations provided, the description carries full responsibility for behavioral disclosure. It clearly states 'Read-only' and 'no mutations, fragments, or variables,' plus it embeds the entire GraphQL schema, which reveals the full range of queries and return types. This is comprehensive transparency that outperforms typical tool descriptions.

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 long due to the embedded schema, but the opening sentence is front-loaded and clear, and the structure (intro, constraints, examples, schema) is logical. Every section earns its place; it could be slightly more concise, but the length is justified by the complexity of a GraphQL API.

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 tool with a single string parameter and no output schema, this description is exceptionally complete. It covers purpose, usage, constraints, and the complete query API. An agent can confidently construct valid queries and anticipate the shape of results based on the schema. There are no critical gaps.

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

Parameters5/5

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

The input schema only defines 'query' as a string. The description compensates richly by providing the complete GraphQL API schema and multiple examples of valid query strings. This gives the agent a deep understanding of how to formulate the query parameter, far exceeding the minimal schema description.

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 clear verb+resource: 'Run a GraphQL query against the collection's structure.' It also distinguishes itself from sibling tools by explicitly stating 'Use this for questions the other tools cannot answer' and listing specific examples such as 'which notes are most connected' and 'which repositories ground the most claims.' This makes the tool's purpose unambiguous and well-differentiated.

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?

The description gives explicit guidance on when to use this tool versus alternatives: 'Use this for questions the other tools cannot answer.' It also provides constraints on usage: 'Read-only: no mutations, fragments, or variables. Inline argument values.' This tells the agent both the appropriate scenarios and the operational boundaries, going beyond simple context.

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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