memory_searchFind stored memories by meaning rather than by exact key.
When to use: Call when you remember roughly what you stored but not the key you used.
Price: US$0.002000 per call.
| Name | Required | Description | Default |
|---|---|---|---|
| q | Yes | Natural-language description of what you are looking for. | |
| tag | No | ||
| limit | No |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint=true and destructiveHint=false, so the read-only safety is covered. The description adds behavioral context beyond that: the search is meaning-based rather than key-based, and it surfaces the per-call price. It doesn't detail result ordering or error behavior, but the essential behavior is disclosed.
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 short sentences, each earning its place: purpose, usage condition, and price. The primary purpose is front-loaded, and there is no redundant or filler content.
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 read-only search tool with three parameters and no output schema, the description covers purpose and when-to-use but omits the meaning of tag and limit, and doesn't describe the return format. The annotations cover safety, so this is a moderate gap rather than a critical one.
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 low: only q has a description ('Natural-language description of what you are looking for.'), while tag and limit lack semantic descriptions. The tool description does not mention tag or limit at all, so it fails to compensate for the coverage gap. It only reinforces the q parameter via the 'by meaning' phrase.
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 opens with a specific verb and resource: 'Find stored memories by meaning rather than by exact key.' This distinguishes memory_search from memory_get (exact key) and memory_list (listing all) with a clear semantic 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?
It provides an explicit when-to-use condition: 'Call when you remember roughly what you stored but not the key you used.' It doesn't name alternatives or when-not-to-use cases, but the condition implies the distinction from key-based lookup.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
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Each tool addresses a distinct resource or action: memory operations are clearly separated from inbox operations, await/park/resume are differentiated by whether the process stays alive or hands off state, and administrative calls like capabilities, whoami, and credit_deposit have non-overlapping purposes. No two tools appear interchangeable.
Most resource operations follow an object_verb pattern (memory_get, hook_create, inbox_list), but core primitives like await, park, resume, and signal are bare verbs, while capabilities and whoami are bare nouns. The style is consistently lowercase snake_case and readable, but the naming convention is mixed rather than uniform.
At 16 tools the set is just past the typical well-scoped range, but each tool maps to a distinct platform capability: identity, inbox, memory, hooks/signals, parking, and billing. It feels slightly heavy rather than bloated.
The core agent workflow is covered end to end: identity creation, credit deposit, hook creation, awaiting triggers, inbox reading, durable memory, and the park/resume round-trip. Minor gaps remain—there is no hook list/delete and no way to cancel a parked state—but they can be worked around rather than blocking.