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mcp-devils-advocate

mcp-devils-advocate

by AleBrito124356
OverviewSchemaSourceRelated ServersDiscussionsAdmin
Python
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export_report

Export a completed review as a shareable decision memo in markdown or JSON, including claim, assessment, rebuttals, mitigations, and next actions checklist.

Instructions

Export a completed review as a shareable decision memo.

'markdown' (default) gives a memo with the claim, context, assessment and its rule, every item with its rebuttal/mitigation/test/response, and a 'Next actions' checklist (counterarguments that still hold, mitigations to carry out, tests to run, conceded points). 'json' gives the raw verdict report.

Args: review_id: The review session id (e.g. "rev-x9k2"). format: "markdown" (alias "md") or "json".

Returns: dict with review_id, format, assessment and the rendered 'content'.

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": {
      "review_id": "<review_id>"
    },
    "name": "export_report"
  }
}

Input Schema

NameRequiredDescriptionDefault
formatNomarkdown
review_idYes

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.2.0

TDQS

A3.8/5.0
Behavior4/5

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

No annotations, so the description carries the full burden, and it does well: it discloses the two output shapes, details what the markdown memo contains, and specifies the return dict keys. It omits permission/auth requirements and whether the review must be in a particular state beyond 'completed', but the behavioral profile is strong.

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?

Front-loaded purpose sentence followed by structured Args/Returns sections. The markdown-format enumeration is somewhat long but each item conveys output content an agent would otherwise have to discover.

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?

For a 2-parameter tool with no output schema, the description supplies purpose, param semantics, and a Returns section describing the dict. Complete enough to invoke correctly; only state preconditions and permissions are unaddressed.

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?

Schema coverage is 0%, so the description must compensate, and it does: it documents review_id with a concrete example ('rev-x9k2') and format including the 'md' alias and the markdown default that the schema only shows as a default value.

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

Purpose4/5

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

States a specific verb and resource: 'Export a completed review as a shareable decision memo.' The 'completed review' qualifier implicitly distinguishes it from start_review/get_verdict, though no sibling is named explicitly.

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

Usage Guidelines3/5

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

Usage is implied by 'completed review' (you export after the review finishes), but there is no explicit when-to-use/when-not or routing to alternatives such as get_verdict or get_review.

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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