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bca-mcp

bca-mcp

by blockchainacademics
OverviewSchemaSourceRelated ServersDiscussionsAdmin
Python
Remote

generate_due_diligence

Initiate a comprehensive due-diligence report covering tokenomics, team, audits, risk, and narrative. Specify entity slug and report depth for async generation.

Instructions

Kick off a full due-diligence report (tokenomics + team + audits + risk + narrative). Required: entity_slug. depth ∈ {light, standard, deep}. Async — returns {job_id, status_url}; poll get_agent_job. Pro tier.

Tool call template

Replace each placeholder with a value matching the input schema. Only required parameters are included; add optional parameters as needed.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {
      "entity_slug": "<entity_slug>"
    },
    "name": "generate_due_diligence"
  }
}

Input Schema

NameRequiredDescriptionDefault
entity_slugYesTarget entity slug.
depthNoDepth of the report: light|standard|deep.standard
focusNoOptional focus areas (e.g. ['tokenomics', 'audits']).

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.3.1

TDQS

A4.2/5.0
Behavior4/5

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

With no annotations, the description discloses the async behavior, return structure (job_id, status_url), and tier requirement. This provides sufficient transparency for a creation-oriented tool.

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 deliver all essential information: purpose and usage instructions. No wasted words, and critical details are front-loaded.

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 output schema, the description explains the return and polling mechanism. It covers the report components but could elaborate on what each component entails. Still, it's adequately complete for an async generation tool.

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

Parameters3/5

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

Schema coverage is 100%, so baseline is 3. The description mentions entity_slug, depth, and focus but adds little beyond the schema's own descriptions. The 'Pro tier' hint is contextual but not parameter-specific.

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 the tool initiates a due-diligence report covering tokenomics, team, audits, risk, and narrative. It distinguishes from sibling tools like get_tokenomics or get_audit_reports which cover individual components.

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 specifies the required entity_slug, acceptable depth values, async nature with polling via get_agent_job, and Pro tier restriction. It implies when to use (full report) versus individual get_* tools, though explicit alternatives are not given.

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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