Fuzzy Memory MCP Server

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

No annotations are provided, so the description carries the full burden of behavioral disclosure. It states the tool deletes observations, implying a destructive mutation, but doesn't describe consequences (e.g., whether deletions are permanent, reversible, or affect related data), permissions required, error handling, or rate limits. For a destructive tool with zero annotation coverage, this is a significant gap.

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 a single, efficient sentence with zero waste. It's front-loaded with the core action and target, making it easy to parse quickly. Every word earns its place by conveying essential information without redundancy.

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 tool's complexity (destructive mutation with 1 parameter but nested structure), lack of annotations, and no output schema, the description is incomplete. It doesn't cover behavioral aspects (e.g., side effects), parameter details, or return values, leaving critical gaps for safe and effective use by an AI agent.

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?

Schema description coverage is 0%, so the schema provides no parameter descriptions. The description mentions 'specific observations from entities' but doesn't explain the input structure (e.g., that 'deletions' is an array of objects with 'entityName' and 'observations' fields). It adds minimal semantic value beyond what's inferred from the tool name, failing to compensate for the coverage gap.

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 action ('Delete') and target ('specific observations from entities in the knowledge graph'), which is a specific verb+resource combination. It distinguishes itself from sibling tools like 'delete_entities' by focusing on observations rather than entire entities. However, it doesn't explicitly contrast with 'add_observations' beyond the verb difference.

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 no guidance on when to use this tool versus alternatives. It doesn't mention prerequisites (e.g., needing existing observations), exclusions, or comparisons to siblings like 'delete_entities' (for removing entire entities) or 'add_observations' (for adding vs. deleting). Usage is implied by the verb 'delete' but lacks explicit context.

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