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submit_diff_review

Send a code diff from a git repository to an AI reviewer for analysis. Specify the provider, optional base branch, and custom instructions to scope the review.

Instructions

Send a code-review job: the MCP server runs git diff inside repo_path and forwards the diff to the chosen provider. The diff content never appears in the LLM's output tokens, only the short tool call does. When base is omitted, auto-detects: dirty working tree -> diff against HEAD; clean tree with main/master/origin-HEAD found -> ${detected}...HEAD; otherwise falls back to diff against HEAD. Pass base explicitly (e.g. "main", a tag, or a SHA) to force a ${base}...HEAD PR-style range. Optional prompt is prepended above the diff so reviewers can scope the review. Default capability is "review" - override if the provider advertises a different tag.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
provider_npubYes
capabilityNoCapability tag advertised by the reviewer. Override if not "review".review
repo_pathNoPath to the git repo. Absolute or relative to the MCP server's working directory..
baseNoOptional base ref (branch, tag, SHA). When set, diffs ${base}...HEAD. When omitted, auto-detects working-tree vs main/master/origin-HEAD.
promptNoOptional instructions prepended above the diff (e.g. "focus on auth flow").
kind_offsetNo
timeout_secsNo
max_price_lamportsNo
allow_outside_cwdNoAllow reviewing a repo outside the MCP server working directory. Off by default - the diff is forwarded to the provider before payment and is invisible in the transcript, so the repo is confined to the working dir subtree unless this is set. Sensitive paths (secret keys, .env, SSH/keypair, ~/.elisym, /proc) are always refused.
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. It discloses that diff content never appears in LLM output tokens, explains auto-detection logic for base, describes the prompt prepending, and details security constraints on allow_outside_cwd and sensitive paths. This is comprehensive for a tool that performs a sensitive operation.

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?

The description is relatively long but every sentence serves a purpose. It front-loads the main action and includes critical details. Slight room for brevity, but no wasted content.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness3/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given the tool's complexity (9 params, no output schema), the description covers the submission process, auto-detection, and security. However, it does not describe the return value or error handling, leaving a gap in completeness.

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 56%, but the description adds significant value beyond the schema, particularly for base (auto-detection logic), prompt (prepending), and allow_outside_cwd (subtree confinement, sensitive paths). It enriches the agent's understanding of parameter usage.

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?

The description clearly states the tool sends a code-review job, runs git diff, and forwards to a provider. It is specific about the resource and action, but does not differentiate from sibling tools like create_job or submit_and_pay_job, which also handle job submission.

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 detailed behavior for the base parameter and auto-detection, but does not give explicit guidance on when to use this tool versus alternatives. No mention of prerequisites or 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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