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 and does well by disclosing key behavioral traits: memories are persisted as markdown files, stored in the '.memories/' directory, indexed by Auggie for semantic retrieval, and have a maximum content length (5000 characters). It doesn't cover potential errors, permissions, or rate limits, but provides substantial operational context.

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 and appropriately sized: it starts with a clear purpose statement, uses bold headings for categories and examples, and ends with storage details. Every sentence adds value without redundancy, and the information is front-loaded with the most important details first.

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?

For a tool with 3 parameters, 100% schema coverage, and no output schema, the description provides good contextual completeness: it explains what memories are, how they're stored and retrieved, categorizes them with examples, and mentions the storage directory. The main gap is lack of information about return values or potential errors, but given the tool's moderate complexity, this is reasonably complete.

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 schema description coverage is 100%, so the baseline is 3. The description adds meaningful context beyond the schema: it explains the semantic meaning of each category with examples ('preferences: Coding style', 'decisions: Architecture decisions', 'facts: Project facts'), provides usage examples that illustrate parameter combinations, and mentions the '.memories/' storage location which isn't in the schema.

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 clearly states the tool's purpose with specific verb ('Store') and resource ('memory'), and distinguishes it from sibling tools like 'list_memories' and 'semantic_search' by focusing on creation rather than retrieval or listing. It explains what memories are and how they're used ('persisted as markdown files and automatically retrieved via semantic search when relevant').

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 provides clear context for when to use this tool through the 'Categories' section and examples, showing appropriate use cases for each category. However, it doesn't explicitly state when NOT to use it or mention alternatives like 'list_memories' for viewing existing memories, though the distinction is implied by the tool's name and purpose.

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