Retrieve relevant memories from a personal knowledge graph using identity-grounded, evidence-based recall. Query your local-first brain for context-aware answers.
Recall memories (neurons) grounded in identity and evidence (ID-RAG + PGMem).
| Name | Required | Description | Default |
|---|---|---|---|
| k | No | ||
| query | Yes | ||
| identity | No |
Changes observed during successful MCP inspections.
v0.1.0Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the full burden. It hints at a mechanism ('ID-RAG + PGMem') but doesn't disclose whether the tool has side effects, what it returns, how the parameters alter behavior, or any required prior context. This is a significant gap for a memory-recall tool.
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 concise sentence, which is structurally efficient. However, the inclusion of unexplained acronyms ('ID-RAG + PGMem') adds noise rather than clarity, so not every element earns its place.
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 tool with no output schema, no annotations, and zero schema descriptions, the description is far too minimal. It doesn't explain what the recalled memories look like, how to interpret results, or how the parameters affect the output, leaving the agent under-informed for correct invocation.
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%, and the description does not explain any parameters. It does not mention 'query', 'k', or 'identity', nor their roles or constraints. The agent receives no semantic information to correctly set these inputs.
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 names a specific action ('Recall') and resource ('memories (neurons)'), but the qualifications 'grounded in identity and evidence (ID-RAG + PGMem)' are cryptic and don't clearly differentiate it from siblings without domain knowledge. It's not a tautology, but it leaves ambiguity about what the tool actually does.
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?
There is no guidance about when to use this tool versus alternatives like celebrum.simulate or celebrum.snapshot. It doesn't state what conditions call for recall or mention any exclusions, leaving the agent to guess based on the name.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.