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closermethod

B2B Buyer-Signal MCP

get_full_pack

Retrieves the entire signal-interpretation library and metadata, enabling fine-tuning or full agent context for B2B buyer intent analysis.

Instructions

Returns the complete signal-interpretation library + metadata. Useful for fine-tuning or full agent context.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Implementation Reference

  • src/main.ts:383-389 (registration)
    Tool registration: 'get_full_pack' is registered in the ListToolsRequestSchema handler with name 'get_full_pack' and an empty input schema (no parameters required). The description says it returns the complete signal-interpretation library + metadata for fine-tuning or full agent context.
        {
          name: "get_full_pack",
          description: "Returns the complete signal-interpretation library + metadata. Useful for fine-tuning or full agent context.",
          inputSchema: { type: "object", properties: {} }
        }
      ]
    }));
  • Handler implementation: When 'get_full_pack' is called, it returns a JSON object containing the pack name, author credentials, and all six signal modules (HIRING_SIGNALS, FUNDING_SIGNALS, TECH_STACK_SIGNALS, LEADERSHIP_SIGNALS, EXPANSION_SIGNALS, SIGNAL_WEIGHTS) plus MCP metadata.
    if (name === "get_full_pack") {
      return {
        content: [{
          type: "text",
          text: JSON.stringify({
            pack: "B2B Buyer-Signal MCP — Complete Library v1.0",
            author: "Elisabeth Hitz",
            credentials: ["10+ years B2B enterprise sales experience", "5+ years EMEA territory ownership"],
            modules: {
              hiring_signals: HIRING_SIGNALS,
              funding_signals: FUNDING_SIGNALS,
              tech_stack_signals: TECH_STACK_SIGNALS,
              leadership_signals: LEADERSHIP_SIGNALS,
              expansion_signals: EXPANSION_SIGNALS,
              signal_weights: SIGNAL_WEIGHTS
            },
            _meta: MCP_META
          }, null, 2)
        }]
      };
    }
Behavior3/5

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

No annotations are provided, and the description only states what is returned, not behavioral aspects like read-only nature, size, or performance implications.

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 concise sentences, front-loaded with purpose, no unnecessary words.

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?

With no parameters and no output schema, the description is adequate for the tool's simplicity, though it could elaborate on what 'library + metadata' includes.

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 input schema has zero parameters and 100% coverage, so the description adds no further parameter info. Baseline of 4 is appropriate.

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 clearly states it returns the complete signal-interpretation library and metadata, distinguishing it from sibling tools that interpret individual signals.

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?

It explicitly mentions use cases (fine-tuning or full agent context) but does not contrast with specific siblings or state when not to use it.

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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