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
Glama MCP Gateway
Connect through Glama MCP Gateway for full control over tool access and complete visibility into every call.
Full call logging
Every tool call is logged with complete inputs and outputs, so you can debug issues and audit what your agents are doing.
Tool access control
Enable or disable individual tools per connector, so you decide what your agents can and cannot do.
Managed credentials
Glama handles OAuth flows, token storage, and automatic rotation, so credentials never expire on your clients.
Usage analytics
See which tools your agents call, how often, and when, so you can understand usage patterns and catch anomalies.
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). 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.
| 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, the description fully discloses behavioral traits: uses multiple models and deterministic detectors, never overrides deterministic findings, depth options affect recall, line limit, payment via x402, and what types of issues it catches (path/contract violations, etc.).
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, starting with a concise summary, followed by rationale for use, then usage details. It is somewhat lengthy but every sentence adds value. It is front-loaded with the most critical information.
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 no output schema and no annotations, the description covers input formats, output structure (pass/fail verdict with issue details), limitations (line limit, split advice), depth options, and pricing. This is sufficient for an agent to use 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?
Schema description coverage is 100%. The description adds valuable context beyond the schema: explains 'payload' can be diff, module, or spec+implementation; 'depth' options are elaborated; 'context' is optional spec/intent. This enhances understanding beyond the schema descriptions alone.
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 inputs (diff, module, spec+implementation) and outputs (structured pass/fail verdict with issue details). It also distinguishes from self-review by explaining why multiple models catch more issues.
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 tells when to use: '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 provides limitations like the ~1,600 line limit and suggests splitting inputs. Depth options and pricing are also covered.
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.
Control your server's listing on Glama, including description and metadata
Access analytics and receive server usage reports
Get monitoring and health status updates for your server
Feature your server to boost visibility and reach more users
For users:
Full audit trail – every tool call is logged with inputs and outputs for compliance and debugging
Granular tool control – enable or disable individual tools per connector to limit what your AI agents can do
Centralized credential management – store and rotate API keys and OAuth tokens in one place
Change alerts – get notified when a connector changes its schema, adds or removes tools, or updates tool definitions, so nothing breaks silently
For server owners:
Proven adoption – public usage metrics on your listing show real-world traction and build trust with prospective users
Tool-level analytics – see which tools are being used most, helping you prioritize development and documentation
Direct user feedback – users can report issues and suggest improvements through the listing, giving you a channel you would not have otherwise
The connector status is unhealthy when Glama is unable to successfully connect to the server. This can happen for several reasons:
The server is experiencing an outage
The URL of the server is wrong
Credentials required to access the server are missing or invalid
If you are the owner of this MCP connector and would like to make modifications to the listing, including providing test credentials for accessing the server, please contact support@glama.ai.
Discussions
No comments yet. Be the first to start the discussion!