SKILLEMALL.ai

AC mcp-implementation-security-review

Review the implementation source code of MCP (Model Context Protocol) servers, clients, and tool handlers against a security baseline — authentication, sessions, rate limiting, input-schema validation, official-SDK usage, RCE vectors, and the OWASP MCP Top 10 — producing a report with file/line evidence. Use this skill when: - Reviewing an MCP server implementation for security before release - Checking a server against the baseline controls (MCP-01 to MCP-05) and the OWASP MCP Top 10 - Auditing tools for RCE vectors (command/code injection, unsafe deserialization, path traversal, SSTI, dependency hijacking, SSRF) - Verifying auth, session, rate-limiting, and input-validation controls on a network-exposed server - Reviewing MCP client code that handles untrusted server responses and session IDs - Requests like "review this MCP server for security" or "is my MCP server implementation secure?"

github/awesome-copilot Agent Skills author: github MIT 1 file body ≈ 4 930 tokens Open the sourcegithub.com analyzed 29 h ago

Review the implementation source code of MCP (Model Context Protocol) servers, clients, and tool handlers against a security baseline — authentication…

As a process C 61/100 · Has gaps — weak spots: inputs and preconditions, progress reporting

AnalyzerAI and agentstype and topics are labelled automatically from the skill text
JSON
Technical rating
A
92/100
safety, quality, tests
Safety 60%
98
Quality 40%
83
Run on models
none yet
Process rating
C
61/100
Has gaps
Inputs and preconditions w 11
0
Progress reporting w 2
0
When it triggers w 12
50
the three weakest of ten parameters · all ten

How to improve

    For the model run — optional
    • Your own cases (evals/evals.json, 4–6 real requests with expected answers): the full check would then run those instead of a model-drafted suite.
    • A spec.yaml with trigger phrases and assertions — a behaviour contract for CI; `skilltest init` writes a template.

    Guard findings · 2

    ✓ No critical or high findings

    Medium and low: 2
    • low Risky intent intent-offensive-security SKILL.md:230
      Offensive-security / dual-use content (legitimate for authorised testing; review intended use)
      **MCP02:2025 — Privilege Escalation via Scope Creep**

    A further 1 matches are quotations in this security skill's documentation and are not counted as findings.

    Files scanned: 1. Evidence is masked. Grey chips explain why severity was lowered.

    Against the Agent Skills spec

    ✓ No remarks against the Agent Skills spec

    Process rating: all ten parameters 61/100

    • 0Inputs and preconditions. Does not say what the process needs to start
    • 0Progress reporting. Says nothing while it works
    • 50When it triggers. No condition that starts the skill
    • 50Failures and branches. 0 branches, has a failure section
    • 60Tools and files. Uses tools (bash, web, python) that frontmatter does not declare
    • 60Result and completion. Output format stated, no completion criterion
    • 70Execution cost. Instruction body is 4930 tokens
    • 100Steps. 66 steps
    • 100Consistency. Name and required fields are in place
    • 100Running it twice. Mutating operations check current state
    • medium Safety rules and hard prohibitions inside a skill: they belong in the system prompt, here they protect nothing

    Everything here is measured from the skill text rather than judged by a model, so the numbers are checkable. A parameter weighs more when it is a more common reason for the process to stall.

    Quality signals

    • +4Description does not say when NOT to use the skill (false activations)
    • +3Description length 904: 120–800 characters recommended
    • +4No input/output examples
    • -2localhost URLs: will not work for another user
    • +1No license
    • +2Single-language instructions
    • +5Description quotes 2 example trigger phrases
    • +4Structure: 26 headings
    • +3Step-by-step instructions: 66 items
    • +3Output format is stated explicitly

    Quality base 70; lint remarks subtract, signals add up to 100. Result: 83.