DeepMem

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

With no annotations, the description carries the transparency burden. It discloses the hybrid scoring mechanism (vector similarity, BM25, entity boost, time decay) and scoping behavior, which goes beyond the bare schema. It does not explicitly state read-only nature or discuss rate limits, but the read behavior is implied and the scoring detail is valuable.

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?

Three sentences, zero fluff, front-loaded with the primary action. Each sentence adds value: purpose, scoring, usage context, and scoping. Efficient and well-structured.

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?

The description covers purpose, scoring, usage, and scoping. However, with no output schema, it does not specify the return format (e.g., list of memory IDs, scores, text). Given 5 parameters and no annotations, it is decent but not fully complete; it could mention the return structure to aid the agent. It is adequate for a search tool but leaves some ambiguity.

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 description must explain parameters. It implicitly covers 'query' (given query) and 'user_id' (scoping), but does not explain 'top_k', 'threshold', or 'api_key'. The parameter names are self-explanatory, but the description does not add meaningful detail beyond the schema's defaults, leaving the agent to guess on threshold semantics.

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 searches memories with semantic search and returns ranked results. It distinguishes from sibling 'deepmem_write' by focusing on retrieval rather than storage, and explicitly says to use it for context from past conversations.

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?

Provides explicit usage guidance: 'Use this to retrieve context from past conversations before responding to the user.' It also notes memory scoping to user_id, implying the need to pass the correct user_id. However, it does not mention when not to use it or alternative approaches, but the sibling-tool context makes the primary use case clear.

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

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 reveals the LLM processing pipeline, the optional infer mode with its cost, persistent storage, and the return of memory IDs. It does not cover permissions or potential side effects, but it offers substantial context beyond a bare write operation.

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 concise and well-structured, with two short paragraphs. It front-loads the core purpose, then details the key option, and closes with return value. Every sentence adds value with no fluff.

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 moderate complexity, the description effectively covers the main operation, the infer distinction, and the return list of IDs. It lacks details on authentication (api_key) and scoping (user_id, agent_id), which are present in the schema but not explained, making it mostly complete but not fully comprehensive.

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?

While the description explains the infer parameter in detail, the schema has 6 parameters and schema description coverage is 0%. The description provides no meaning for run_id, api_key, user_id, agent_id, or messages beyond the overall context. It compensates only for infer, leaving the other five parameters unexplained, which is insufficient for low coverage.

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 writes conversation messages to DeepMemory for fact extraction and persistent storage, using a specific verb and resource. It distinguishes itself from the sibling deepmem_search by focusing on the write/storage operation rather than retrieval.

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 explains the core behavior and the infer parameter trade-off but does not explicitly state when to prefer this tool over deepmem_search or any other alternative. Usage is implied rather than directly stated, and no exclusions or prerequisites are mentioned.

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