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llm-code-security-review-mcp

llm-code-security-review-mcp

by drewrukin
OverviewSchemaSourceRelated ServersDiscussionsAdmin
Python
Local

report_done

Submit structured review results and verify that all assigned source code was read. If coverage is incomplete, returns the exact missing read; otherwise, accepts the outcome for the current plan step.

Instructions

Submit the structured result. This call mechanically verifies source coverage and returns the exact missing read when incomplete. For NO FINDINGS, finding_ids, evidence, and artifacts must all be empty. For FINDING or CORROBORATION, include only concise source ranges needed to prove the security effect. After an accepted result, immediately call get_task {} and continue until COMPLETE; after COMPLETE, call status {} and use only its diagnostics for final totals.

Tool call template

Replace each placeholder with a value matching the input schema. Only required parameters are included; add optional parameters as needed.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {
      "step_id": "<step_id>",
      "outcome": "<outcome>",
      "summary": "<summary>",
      "finding_ids": "<finding_ids>",
      "evidence": "<evidence>",
      "artifacts": "<artifacts>"
    },
    "name": "report_done"
  }
}

Input Schema

NameRequiredDescriptionDefault
outcomeYes
step_idYes
summaryYes
evidenceYes
artifactsYes
finding_idsYes

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.5.0

TDQS

A4.4/5.0
Behavior4/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

The description discloses meaningful behavior beyond the annotations: it mechanically verifies source coverage, returns the exact missing read when incomplete, and enforces content constraints per outcome. This is especially valuable because annotations only indicate non-destructiveness and closed-world behavior, not the verification workflow.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is dense but every sentence earns its place: purpose, validation behavior, per-outcome payload rules, and next-step workflow. It is front-loaded with the core action and avoids filler.

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

Completeness4/5

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

For a structured submission tool with no output schema, the description provides strong workflow context and error behavior. It could be more complete by explaining what an 'accepted result' looks like and clarifying step_id/summary expectations, but overall it gives enough for an agent to operate correctly.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters3/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

With 0% schema description coverage, the description partially compensates by explaining outcome-specific requirements for finding_ids, evidence, and artifacts, and by describing evidence as concise source ranges. However, it leaves step_id, summary, and the evidence note field semantically unexplained, so the compensation is incomplete.

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 opens with a specific verb and resource: 'Submit the structured result.' It goes on to explain that the tool verifies source coverage and reports missing reads, making its role clear. It also distinguishes itself from siblings by locating it in the workflow between get_task and status.

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?

The description provides concrete when-to-use guidance: submit results, then call get_task until COMPLETE, then call status for final totals. It also gives outcome-specific formatting rules for NO FINDINGS versus FINDING/CORROBORATION, leaving little ambiguity about when and how to invoke the tool.

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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