MCP-Assisted Semantic Analysis

This reference covers how to configure, query, and interpret Serena MCP evidence during the zeroize-audit semantic pass. For compile DB generation and flag extraction, refer to the compile-commands reference (loaded separately from SKILL.md).

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Preconditions

Before running any MCP queries, the following must hold. These are verified during Step 1 (Preflight) in task.md — do not re-run preflight here, just confirm the relevant outputs:

Rust note: Cargo does not natively produce compile_commands.json. Use bear -- cargo build or bear -- cargo check to generate it. rust-project.json is not a substitute in this workflow.

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Configuring Serena

The plugin.json registers Serena as the serena MCP server, launched via uvx. Serena wraps language servers (clangd for C/C++) and exposes semantic analysis as high-level MCP tools. It auto-discovers compile_commands.json from the project working directory.

Prerequisites:

• uvx must be on PATH (installed with uv — see https://docs.astral.sh/uv/)
• Serena is fetched and run automatically via uvx --from git+https://github.com/oraios/serena
• No separate clangd installation is required — Serena manages language server dependencies internally

Verify before querying:

{baseDir}/tools/mcp/check_mcp.sh \\
  --compile-db /path/to/compile_commands.json

A non-zero exit means MCP is unreachable. Apply the preflight failure behavior above.

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MCP Tool Reference

Serena abstracts LSP methods into higher-level, symbol-name-based tools. Unlike raw LSP, you query by symbol name rather than file position.

Mapping from previous LSP-based queries:

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Query Order

Run queries in this order so each step's output informs the next. All queries for a given TU should complete before moving to the next TU.

Step 0 — Activate the project (activate_project)

This must be called once before any other Serena tool. Pass the repository root path. If activation fails, treat MCP as unavailable.

Tool: activate_project
Arguments:
  project: "/path/to/repo"

Expected: confirmation that the project is active. Serena will start indexing the codebase (including launching clangd if needed). Wait for activation to succeed before proceeding.

Step 1 — Resolve symbol definition (find_symbol)

Establishes the canonical declaration location and type information used in all subsequent queries.

Tool: find_symbol
Arguments:
  symbol_name: "secret_key"
  include_body: true

Expected: result with file, line, kind, symbol name, and body content. The body provides type information (array sizes, struct layout) needed for wipe-size validation in Step 3. Store this as the canonical location for Steps 2–4.

If the symbol name is ambiguous, narrow with file_path:

Tool: find_symbol
Arguments:
  symbol_name: "secret_key"
  file_path: "src/crypto.c"
  include_body: true

Step 2 — Collect all use sites (find_referencing_symbols)

Finds every location where the sensitive symbol is referenced. Use these to locate adjacent wipe calls and detect copies to other scopes.

Tool: find_referencing_symbols
Arguments:
  symbol_name: "secret_key"

Expected: list of referencing symbols with file, line, symbol, and kind. For each reference in a file other than the source TU, check that file for cleanup. References in generated files (build directory) can be filtered by source directory prefix.

Step 3 — Resolve type and size

Type information is returned as part of find_symbol results (Step 1). If you need to resolve a typedef or follow a type alias chain, look up the type name directly:

Tool: find_symbol
Arguments:
  symbol_name: "secret_key_t"
  include_body: true

Use this to validate wipe sizes — a sizeof(ptr) bug will be apparent when the symbol body reveals uint8_t [32] but the wipe uses sizeof(uint8_t *).

Step 4 — Trace callers and cleanup paths

Use find_referencing_symbols on the function containing the sensitive object to find callers that may hold their own copy of the secret. Use it on wipe wrapper functions to find cleanup paths.

Tool: find_referencing_symbols
Arguments:
  symbol_name: "process_key"

For outgoing calls (what does this function call?), read the function body from find_symbol output and resolve each called function:

Tool: find_symbol
Arguments:
  symbol_name: "process_key"
  include_body: true

Then for each function called within the body:

Tool: find_symbol
Arguments:
  symbol_name: "cleanup_secret"

Step 5 — Normalize output

Before using any MCP results in confidence scoring or finding emission, normalize:

python {baseDir}/tools/mcp/normalize_mcp_evidence.py \\
  --input /tmp/raw_mcp_results.json \\
  --output /tmp/normalized_mcp_results.json

The normalizer produces a consistent schema consumed by the MCP semantic pass and subsequent confidence gating steps.

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Interpreting Responses

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Confidence Scoring

MCP evidence contributes one signal toward the 2-signal threshold for confirmed findings (see SKILL.md Confidence Gating). Tag each piece of evidence with its source:

MCP evidence alone (1 signal) produces likely. MCP + one additional signal (source, IR, CFG, or ASM) produces confirmed.

Mandatory downgrades — applied by apply_confidence_gates.py after all evidence is collected:

Apply downgrades after all evidence is collected, not during querying. Do not suppress findings preemptively — emit at needs_review rather than dropping them.

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Post-Processing

After collecting all MCP evidence and running IR/ASM/CFG analysis, apply confidence gates mechanically:

python {baseDir}/tools/mcp/apply_confidence_gates.py \\
  --input /tmp/raw-report.json \\
  --out /tmp/final-report.json \\
  --mcp-available \\
  --mcp-required-for-advanced

Omit --mcp-available if MCP was unreachable. Omit --mcp-required-for-advanced if mcp_required_for_advanced=false in the run config. The script applies all downgrade rules from SKILL.md and outputs gated findings ready for the report assembly phase.