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giaminhgist

deepseek-mcp

by giaminhgist

deepseek_review

Review working, staged, or HEAD diffs and named files. Get prioritized findings with severity, confidence, and path:line evidence for a first-pass code review.

Instructions

First-pass code review by the DeepSeek worker of working/staged/head diffs or named files. Findings carry severity, confidence, and path:line evidence. Output is advisory — Claude does final review — and ends with a DeepSeek token usage footer.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
taskNoOptional additional review instruction.
pathsNoOptional repository-relative path filters (diff scopes) or files under review (scope=paths).
scopeNoReview scope. One of: working | staged | head | paths.working
review_focusNoFocus areas. Subset of: correctness | security | performance | tests | maintainability.

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.1.0

TDQS

A4.2/5.0
Behavior4/5

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

With no annotations provided, the description carries the full burden and does meaningful work: it discloses the advisory nature, the final-review handoff to Claude, the structure of findings (severity, confidence, path:line evidence), and the token usage footer. It does not explicitly state that the operation is read-only, but the review framing and lack of mutation language are reasonably transparent.

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 dense sentences carry the purpose, scope, output structure, advisory role, and footer behavior with no filler. The most important identifying information is 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?

For a tool with no required parameters, an output schema, and clear parameter documentation, the description covers the essential role, scope, and output characteristics. It falls short only in not giving explicit usage boundaries against the sibling tools, which is a minor gap given the strong schema coverage.

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 description coverage is 100%, so the baseline is 3. The description adds only marginal semantic value by mapping "working/staged/head diffs or named files" to the scope choices, but it does not meaningfully elaborate on task, paths, or review_focus beyond what the schema already provides.

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 names a specific verb and resource: "First-pass code review by the DeepSeek worker of working/staged/head diffs or named files." It clearly separates this from the sibling tools by framing it as an advisory review rather than a general task or usage query, so an agent can tell what it is for.

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?

The description gives clear context: this is a first-pass code review whose output is advisory and followed by Claude's final review. This implies when it should be used, though it does not explicitly name alternatives or state when-not-to-use conditions relative to deepseek_task or deepseek_usage.

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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