memory_recall_tool

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: it uses 'ChromaDB vector similarity' for search, applies filters, and optionally expands results via 'graph edges' with 'configurable multi-hop traversal.' It also details the return structure, including success status, memory lists, and expanded data. However, it lacks information on potential side effects (e.g., if this is read-only or has performance impacts) and error handling, which would elevate the score.

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 a concise purpose statement, followed by detailed parameter and return explanations. Every sentence earns its place by adding value, such as clarifying search mechanics and parameter defaults. However, it could be more front-loaded by summarizing key capabilities before diving into details, and the 'Returns' section is lengthy but necessary given the output complexity.

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 (11 parameters, no annotations, but with an output schema), the description is highly complete. It covers the purpose, detailed parameter semantics, and behavioral aspects like search method and graph expansion. The output schema exists, so the description appropriately explains return values without redundancy. This provides sufficient context for an agent to use the tool effectively.

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 0%, so the description must compensate fully. It does so excellently by providing detailed semantics for all 11 parameters in the 'Args' section, explaining each parameter's purpose, defaults, optionality, and valid values (e.g., 'memory_type' examples, 'decay_factor' meaning, edge type lists). This adds significant meaning beyond the bare schema, making parameters clear and actionable for an agent.

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: 'Recall memories using semantic search with optional multi-hop graph expansion.' It specifies the verb ('recall'), resource ('memories'), and method ('semantic search' with 'graph expansion'). However, it doesn't explicitly differentiate from sibling tools like memory_list_tool or memory_context_tool, which likely serve different recall purposes.

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 through its parameter explanations (e.g., 'optional multi-hop graph expansion'), suggesting it's for advanced recall with filtering and relationship traversal. However, it doesn't explicitly state when to use this tool versus alternatives like memory_list_tool (which might list without search) or memory_context_tool (which might provide context without expansion), leaving the guidelines somewhat implicit.

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