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
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"). |
TDQS
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.
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.
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.
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.
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.
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.
Frequently Asked Questions
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/.well-known/glama.jsonon the same origin as the connector, then choose Check HTTP challenge.DNS challenge — works when you control DNS but cannot change the server. Generate a token, create the exact TXT record Glama shows, wait for it to propagate, then choose Check DNS challenge.
After verification, Glama sends a confirmation email and gives you access to listing details, thumbnails, health checks, and analytics. Keep the HTTP file or DNS record in place: Glama periodically checks it and ownership remains verified while the token is discoverable.
The HTTP ownership file has this structure:
{
"$schema": "https://glama.ai/mcp/schemas/connector.json",
"claim": "glama_claim_..."
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If verification fails, confirm that you copied the current token exactly. The HTTP file must be public, return valid JSON with a successful HTTP response, and stay on the connector's origin. DNS changes may need more time to propagate. A claim cannot transfer to a different origin or hostname: if the connector target changes, Glama starts the grace period and the new target must be claimed separately after the previous claim is released.
For a connector linked to the official MCP Registry, registry updates continue to replace its name, description, and URL by default. After claiming, open Manage connector and enable Use Glama listing details as the source of truth if edits made on Glama should be preserved. Categories and thumbnails are always managed on Glama; registry linkage and technical connection settings continue to sync.
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Claim ownership of the server listing
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Keep tool definitions clear and complete to earn a high Tool Definition Quality Score (TDQS)
Route real usage through the Glama Gateway; more recorded successful server uses also improve the ranking
For users:
Full audit trail – every tool call is logged with inputs and outputs for compliance and debugging
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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
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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
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TDQS
Only one tool exists, so no ambiguity between tools is possible.
With a single tool, naming is trivially consistent; the name 'review_code' follows a clear verb_noun pattern.
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.
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.