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
Average 4.8/5 across 1 of 1 tools scored.
Only one tool exists, so there is no possibility of ambiguity between tools. The tool's purpose is clearly defined.
With a single tool, naming is inherently consistent. The name 'review_code' follows a clear verb_noun pattern.
The server has only one tool, which is borderline for a service that appears to offer a complex, multi-model code review. However, it's plausible that all functionality is encapsulated in one tool.
The single tool covers multiple review scenarios (diff, module, spec+implementation) and returns structured results. Minor gaps could include lack of separate configuration tools, but core review functionality appears complete.
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). Paid per call via x402 (USDC on Base); the price is announced in the 402 response before any charge.
| 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
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description fully discloses behavioral traits: it uses a multi-model panel, deterministic detectors, never overrides deterministic findings, catches specific issue types, returns structured verdicts, charges via x402, and offers depth options. This comprehensive disclosure compensates for the absence of annotations.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is well-structured with a clear opening sentence, a detailed 'why call this' section, and usage instructions. While it is somewhat lengthy, every sentence contributes meaningful information, making it appropriate for a complex tool.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's complexity and the absence of an output schema, the description thoroughly covers input types, output structure (pass/fail verdict with issue details), behavioral traits, use cases, depth options, and pricing. It leaves no critical gaps for an AI agent to understand and invoke the tool correctly.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The input schema has 100% description coverage, so baseline is 3. The description adds value by clarifying the payload's acceptable forms (diff, module, spec+implementation) and explaining the depth choices, though the schema already captures these. The context parameter is also elaborated. Thus, a score of 4 is appropriate.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool's purpose: 'Adversarial multi-model code review.' It specifies what can be submitted (diff, module, or spec+implementation) and what is returned (structured pass/fail verdict with issue details). Although there are no sibling tools to distinguish from, the description effectively communicates the unique value proposition relative to self-review.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description explicitly explains when to use this tool: 'Call it before shipping or merging, as a second opinion on a risky change, or as a gate in an autonomous build loop.' It also contrasts with self-review, providing a clear rationale for choosing this tool over alternatives.
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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{
"$schema": "https://glama.ai/mcp/schemas/connector.json",
"maintainers": [{ "email": "your-email@example.com" }]
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