A
license-
qualityA
maintenanceMulti-model AI code review with structured debate. OpenAI, Gemini, Grok, and Claude review your code in parallel, then an anonymized arbitration produces a single consensus summary.
MIT
review-mcp
Server Details
Multi-model code review: a panel of models + detectors return a pass/fail verdict. Paid via x402.
- Status
- Healthy
- Last Tested
- Transport
- Streamable HTTP
- URL
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Tool Definition Quality
A4.7/5.0
Tool DescriptionsA
Average 4.9/5 across 1 of 1 tools scored.
Server CoherenceA
Disambiguation5/5
Only one tool exists, so no ambiguity between tools is possible.
Naming Consistency5/5
With a single tool, naming is trivially consistent; the name 'review_code' follows a clear verb_noun pattern.
Tool Count3/5
The server has only one tool, which feels thin for general use but is borderline acceptable given the comprehensive description and the focused scope of code review.
Completeness4/5
The tool covers the core code review task thoroughly, with options for depth and handling of different input types, but lacks any auxiliary tools for managing or listing reviews.
Available Tools
1 toolreview_codeAInspect
Adversarial multi-model code review. Submit a diff, a module, or a spec+implementation and get back a structured pass/fail verdict with each issue's type, severity, location, explanation, and suggested fix.
Why call this instead of reviewing your own output: a single model shares its blind spots with itself. This routes your code through a panel of different models plus a set of deterministic detectors, catching what self-review misses — path/contract violations, module incoherence (dangling imports, broken cross-references), syntax and call-arity regressions in a diff's post-image, and 'prose instead of tool calls' (output that describes an action rather than emitting it). The panel adds semantic judgment on top and never overrides a deterministic finding.
Call it before shipping or merging, as a second opinion on a risky change, or as a gate in an autonomous build loop. Choose depth='fast' (one model, low latency) or 'deep' (full panel, higher recall). Deep review audits files of any size in milestone chunks so every panel model contributes; the price (shown in the 402) and the payment window scale with file size. Per-call limit ~1,600 lines — larger inputs return 413, so split by file/module and call once per file. Paid per call via x402 (USDC on Base); the price is announced in the 402 response before any charge.
ParametersJSON Schema
| Name | Required | Description | Default |
|---|---|---|---|
| depth | No | "fast" = single-model, low latency; "deep" = full panel, higher recall (default "deep"). | |
| context | No | optional spec/intent, related interfaces, constraints, or what the change should do. | |
| payload | Yes | a unified diff, a complete module/file, or a spec plus its implementation (include enough context lines for diffs). | |
| language | Yes | primary language of the payload (e.g. "typescript", "python"). |
Tool Definition Quality
A4.9/5.0
Behavior5/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations provided, so description carries full burden. It discloses use of multiple models, deterministic detectors, depth options, milestone chunking, and that deterministic findings are never overridden. Also explains payment flow.
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?
Description is lengthy but well-structured and front-loaded with core purpose. Every sentence provides necessary detail; however, it could be slightly more concise without losing value.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness5/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Despite no output schema, description fully explains return format: 'structured pass/fail verdict with issue type, severity, location, explanation, and suggested fix.' Also covers line limit, depth options, and cost, making it complete for agent decision-making.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters5/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is 100%, baseline is 3. Description adds significant context: explains payload as 'unified diff, complete module/file, or spec+implementation', context as optional spec/intent, depth options with defaults, and language as primary. Also adds usage constraints like line limit and payment.
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 performs 'adversarial multi-model code review' and specifies inputs (diff, module, spec+implementation) and output (structured pass/fail verdict). It distinguishes itself from self-review by explaining the multi-model advantage.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines5/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
Explicit when-to-use: 'before shipping or merging, as a second opinion on a risky change, or as a gate in an autonomous build loop.' Also provides limitations: line limit (~1,600 lines), return 413 for larger inputs, and payment via x402.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Claim this connector by publishing a /.well-known/glama.json file on your server's domain with the following structure:
{
"$schema": "https://glama.ai/mcp/schemas/connector.json",
"maintainers": [{ "email": "your-email@example.com" }]
}The email address must match the email associated with your Glama account. Once published, Glama will automatically detect and verify the file within a few minutes.
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