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list_conventions

Identify actual naming conventions from your codebase including prefixes, suffixes, conversions, and casing rules with real examples. Get accurate style guidance for new code or onboarding.

Instructions

List the project's actual naming conventions detected from code — prefix patterns (nb_, is_, has_), suffix patterns, conversion patterns (x_to_y), and casing rules, each with real examples from the codebase. More accurate than guessing from a few files. Use when asked about coding style, before writing new code, or when onboarding to a project.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Implementation Reference

  • The tool 'list_conventions' is registered in an iteration over all tool definitions. Each tool definition (including list_conventions) is registered via pi.registerTool with its name prefixed as 'ontomics_', and the execute handler calls out to an external MCP server (the ontomics binary) via McpClient.callTool.
    for (const def of toolDefs()) {
      pi.registerTool({
        name: `ontomics_${def.mcpName}`,
        label: def.label,
        description: def.description,
        promptSnippet: def.promptSnippet,
        promptGuidelines: [
          "Use ontomics tools BEFORE grep/glob for semantic codebase questions.",
        ],
        parameters: def.parameters,
        async execute(_toolCallId, params, _signal, onUpdate, _ctx) {
          onUpdate?.({
            content: [{ type: "text", text: `Querying ontomics: ${def.mcpName}...` }],
          });
          try {
            const mcp = await getClient();
            const text = await mcp.callTool(def.mcpName, cleanArgs(params));
            return { content: [{ type: "text", text }] };
          } catch (err) {
            throw new Error(
              `ontomics ${def.mcpName} failed: ${err instanceof Error ? err.message : String(err)}`,
            );
          }
        },
      });
    }
  • The schema/definition for 'list_conventions' tool: mcpName: 'list_conventions', label: 'List Conventions', description explaining it lists naming conventions, and empty parameters (Type.Object({})).
    {
      mcpName: "list_conventions",
      label: "List Conventions",
      description:
        "List the project's actual naming conventions detected from code — " +
        "prefix/suffix patterns, conversion patterns, casing rules.",
      promptSnippet:
        "ontomics_list_conventions: detected naming conventions with examples",
      parameters: Type.Object({}),
    },
  • The execute handler for all tools (including list_conventions) — it calls the external MCP server via mcp.callTool(def.mcpName, cleanArgs(params)) and returns the text result. The actual logic lives in the ontomics binary process.
    async execute(_toolCallId, params, _signal, onUpdate, _ctx) {
      onUpdate?.({
        content: [{ type: "text", text: `Querying ontomics: ${def.mcpName}...` }],
      });
      try {
        const mcp = await getClient();
        const text = await mcp.callTool(def.mcpName, cleanArgs(params));
        return { content: [{ type: "text", text }] };
      } catch (err) {
        throw new Error(
          `ontomics ${def.mcpName} failed: ${err instanceof Error ? err.message : String(err)}`,
        );
      }
    },
Behavior3/5

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

No annotations provided, so description must carry burden. It discloses that conventions are detected from code and provides real examples, but does not state read-only nature or side effects. Adequate but not explicit.

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 describes outputs, second provides usage guidance. No filler, front-loaded with key information.

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?

Given no parameters or output schema, description covers what is returned (patterns and examples) and when to use. Could benefit from mentioning return structure, but adequate for the tool's simplicity.

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?

No parameters; schema coverage is trivially 100%. Baseline for 0 params is 4. Description adds value by explaining the tool's return content, so score holds at 4.

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 specifies the verb 'list' and resource 'naming conventions', detailing prefix, suffix, conversion, and casing patterns with examples. It clearly distinguishes from sibling check_naming by focusing on an overview.

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?

Explicitly states when to use: when asked about coding style, before writing new code, or when onboarding. Does not mention alternatives or when not to use, but context 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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