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AINative-Studio

ainative-opencode-memory-mcp

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opencode_recall_context

Retrieve the most relevant remembered context for your current coding task, restoring architecture decisions, conventions, and gotchas from past sessions to reload agent knowledge.

Instructions

Pull the most relevant remembered context for the current coding task — call this at the START of a session to reload what the agent knew.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
taskYesThe task or file you're working on
limitNoMax memories (default 8)
Behavior2/5

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 says 'Pull' which implies a read operation, but it does not explicitly state that it is non-destructive, what it returns, or any side effects. The phrase 'reload what the agent knew' adds context but lacks concrete behavioral guarantees.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is a single, front-loaded sentence that conveys purpose and usage without any redundant words. Every phrase earns its place, and the em-dash separates the core action from the explicit usage instruction.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness3/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

The tool is simple (2 params, no output schema), and the description covers purpose and timing, but it doesn't explain what the returned 'context' looks like or how it relates to stored memories. It also doesn't differentiate from search_memory beyond the stated use case, leaving some practical ambiguity for an agent.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters3/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

The input schema fully describes both parameters ('task' and 'limit') with clear descriptions, giving 100% schema coverage. The tool description itself adds no additional parameter-level semantics beyond what the schema already provides, so the baseline score of 3 is appropriate.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description uses a specific verb ('Pull') and resource ('remembered context for the current coding task'), and distinguishes itself from siblings like search_memory by focusing on task-relevance and session startup. It clearly states what the tool does and when it should be used.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines4/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description explicitly instructs the agent to call this at the START of a session to reload prior knowledge, providing a clear usage context. It does not, however, mention exclusions or explicitly compare against sibling tools like search_memory, so it falls short of a 5.

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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