The venue server must be available to every project contributor, so it belongs at project scope. Claude Code stores project-scoped MCP configurations in the repository-root .mcp.json file, which can be committed to version control and shared with the team.
The experimental playlist server should remain private. Local-scoped MCP servers are stored under the current project's entry in ~/.claude.json , while user-scoped servers are also stored in ~/.claude.json but load across all projects. Because this server is described as a personal experiment, either private scope is conceptually appropriate; among the listed choices, option B correctly separates shared project configuration from private personal configuration. ( https://code.claude.com/docs/en/mcp ) Option A prevents the team from receiving the venue configuration. Option C reverses the intended visibility.
Option D exposes the experimental server configuration to other contributors and may commit machine- specific commands or credentials.
The preferred commands would specify --scope project for the shared venue server and --scope local for an experiment limited to the current repository. Project-scoped servers also require user approval before Claude Code activates configurations received through .mcp.json .
Official references/topics: MCP Installation Scopes; Project Scope; Local Scope; .mcp.json ; ~/.claude.json .

The prompt's conservative reporting policy is directly causing the low recall. Claude may discover a legitimate race condition during analysis but suppress it because it cannot satisfy the instruction to report only issues about which it is certain. Option C separates two objectives that should not be conflated: broad defect discovery and strict acceptance filtering.
Anthropic's current code-review prompting guidance explicitly recommends reporting every issue, including uncertain or lower-severity findings, assigning confidence and severity metadata, and allowing a separate verification stage to filter them. This maximizes recall while retaining control over developer-facing noise.
Option A leaves the suppression policy intact, so additional examples cannot guarantee that discovered problems will be reported. Option B improves recall but relies on historical category-level suppression, which can discard genuine findings that happen to belong to noisy categories. Option D can improve analysis quality but does not remove the instruction responsible for withholding findings. A dedicated finding stage followed by an independently configurable verification or thresholding stage produces measurable recall and precision controls without forcing one model call to optimize competing objectives.

Option B creates a targeted correction loop using information the first extraction did not have: the validator's precise failure report. Anthropic's prompting guidance describes prompt chaining as appropriate when an application must inspect intermediate output or enforce a specific pipeline, with self-correction following the pattern generate, review against criteria, and refine. Here, deterministic validators identify arithmetic mismatches and invalid vendor-ID formats. Returning the source document, original extraction, and explicit errors lets Claude revise only the defective fields while preserving valid data. The corrected record must then be validated again before acceptance, with a bounded retry count and human-review fallback. Option A is unsafe because automatically recalculating a total may overwrite the amount actually printed on the invoice; a mismatch can originate in an OCR or line-item extraction error. Option C repeats the same extraction without explaining what failed, wasting calls and relying on chance. Option D may prevent some format violations, but JSON Schema cannot express every cross-field arithmetic relationship or external vendor-registry rule. A validator-guided second pass therefore reduces manual review without silently changing source facts, while keeping deterministic code-not the model-as the final acceptance authority.

The search must first establish the function's complete naming surface. Reading the core definition and every wrapper or re-export reveals aliases such as computeOrderTax , names introduced through intermediate modules, and potentially different import paths. Once those names are known, the agent can search the entire codebase for each exposed identifier and inspect the resulting call sites.
Claude Code's Grep tool searches patterns inside file contents, whereas Read provides the surrounding implementation needed to determine whether a wrapper delegates to, renames, or conditionally invokes the original function. These tools are complementary: Read establishes semantic identity; Grep provides broad reference discovery. ( https://code.claude.com/docs/en/tools-reference?utm_source=chatgpt.com ) Option B misses every caller using a wrapper alias. Option A may find importing files, but import-level discovery is indirect and can miss re-exports, namespace imports, dependency injection, or calls made through a locally renamed symbol. It also requires unnecessary manual inspection of every consumer of the module. Option D relies on documentation, which may be incomplete or stale and cannot prove that all executable references have been found.
Before removal, the agent should also inspect tests, dynamic registrations, configuration-driven references, and generated code where relevant. The defining requirement is to map the alias chain first and then search all discovered public names.
Official references/topics: Read and Grep Tool Behavior; Symbol and Alias Discovery; Codebase Reference Tracing.

Option A establishes the caching architecture before consuming thousands of implementation lines. The base class or protocol reveals the supported operations, lifecycle, cache-key rules, and invalidation contract.
Imports, subclasses, registrations, and call sites can then direct the agent toward only those concrete implementations relevant to the proposed trigger. Anthropic's effective context-engineering guidance explains that context is finite and that recall degrades as irrelevant tokens accumulate. It identifies Glob and Grep as mechanisms for retrieving code just in time rather than loading an entire repository upfront. Option B assumes file size or naming determines architectural importance; a small interface may define the system while a large service file contains incidental caching calls. Option C loads approximately 8,000 lines before establishing which portions matter. Option D is overly lexical and can miss invalidation implemented through event publication, key-version changes, inherited methods, or generically named hooks. Dependency-guided exploration provides a compact conceptual model first and expands the investigation only when concrete relationships show that additional files are relevant.