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kb_get_professional

Retrieve professional information from persistent knowledge storage to provide AI agents with context about identity, work, and preferences across sessions.

Instructions

Get professional information

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Implementation Reference

  • The main handler for the kb_get_professional tool in the MCP server's CallToolRequestSchema switch statement. It retrieves professional information using km.getProfessional() and returns it as formatted JSON text content.
    case 'kb_get_professional': {
      return {
        content: [
          {
            type: 'text',
            text: JSON.stringify(km.getProfessional(), null, 2)
          }
        ]
      };
    }
  • The tool schema definition including name, description, and empty input schema (no parameters required). This is part of the tools array used for tool listing.
      name: 'kb_get_professional',
      description: 'Get professional information',
      inputSchema: {
        type: 'object',
        properties: {}
      }
    },
  • The helper method getProfessional() in KnowledgeManager class that returns a shallow copy of the professional information from the internal knowledge base state.
    getProfessional(): ProfessionalInfo {
      return { ...this.kb.professional };
    }
Behavior2/5

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

No annotations are provided, so the description carries the full burden of behavioral disclosure. 'Get professional information' implies a read-only operation, but it doesn't specify whether this requires authentication, involves rate limits, returns structured or unstructured data, or has any side effects. For a tool with zero annotation coverage, this lack of behavioral detail is a significant gap, though not contradictory.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness3/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is a single phrase, 'Get professional information', which is concise but under-specified. While it avoids unnecessary verbosity, it fails to provide essential context that would help an agent understand the tool's scope or differentiate it from siblings. The brevity comes at the cost of clarity, making it inefficient rather than optimally concise.

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?

Given the tool's simplicity (0 parameters, no output schema, no annotations), the description is incomplete. It doesn't explain what 'professional information' entails, how it's formatted, or how it relates to other knowledge base tools. Without annotations or output schema, the description should provide more context about the return value and usage scenarios, but it does not, leaving gaps for agent interpretation.

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 has 0 parameters, and schema description coverage is 100%, so there are no parameters to document. The description doesn't need to compensate for missing parameter information, and it appropriately avoids discussing nonexistent inputs. A baseline score of 4 is assigned since no parameter semantics are required, and the description doesn't introduce confusion about inputs.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose2/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description 'Get professional information' is a tautology that restates the tool name 'kb_get_professional' without adding meaningful specificity. It doesn't clarify what type of professional information is retrieved (e.g., contact details, work history, credentials) or how it differs from sibling tools like 'kb_get_personal' or 'kb_get_all'. While it indicates a retrieval action, the purpose remains vague and undifferentiated.

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

Usage Guidelines2/5

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

The description provides no guidance on when to use this tool versus alternatives. It doesn't mention prerequisites, context for usage, or comparisons to sibling tools such as 'kb_get_personal' for personal data or 'kb_get_all' for comprehensive information. Without any usage context, the agent must infer applicability from the tool name alone, which is insufficient for informed selection.

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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