Context Engine MCP Server

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

With no annotations provided, the description carries the full burden of behavioral disclosure. It effectively describes the tool's behavior by outlining the multi-tier validation process, including deterministic and heuristic checks, and mentions automatic secret scrubbing with an option to disable it. This provides clear insight into what the tool does, though it lacks details on output format, error handling, or performance aspects like rate limits.

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?

The description is well-structured with bullet points for clarity and uses bold headings for tiers, making it easy to scan. It is front-loaded with the main purpose and efficiently details the validation steps without unnecessary fluff. A minor improvement could be condensing some points, but overall, it is concise and informative.

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?

Given the complexity of a 4-parameter tool with no annotations and no output schema, the description is moderately complete. It covers the validation process and secret scrubbing but does not explain return values, error cases, or how different content types affect validation. This leaves gaps for an AI agent to fully understand tool behavior, especially without structured output information.

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?

The input schema has 100% description coverage, so the baseline is 3. The description adds value by explaining that validation is context-aware based on content type (implied by the 'content_type' parameter) and mentions secret scrubbing, which relates to the 'scrub_secrets' parameter. However, it does not elaborate on how parameters like 'file_path' influence validation, keeping it slightly above baseline.

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 explicitly states the tool's purpose as 'Run multi-tier validation on content' and provides specific details about what each tier checks, including deterministic checks (balanced brackets, valid JSON, non-empty content) and heuristic checks (TODO/FIXME detection, console statements, hardcoded URLs, line length). It clearly distinguishes this from sibling tools like 'scrub_secrets' by mentioning that secret scrubbing is included but can be disabled, indicating a broader validation scope.

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?

The description implies usage by detailing the types of validation performed (e.g., for code or text content) and mentions that secret scrubbing is automatic but can be disabled, suggesting it's useful for content review or quality checks. However, it does not explicitly state when to use this tool versus alternatives like 'scrub_secrets' or 'run_static_analysis', nor does it provide exclusions or prerequisites, leaving some ambiguity.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.