Store a key->value lookup table as its own content-addressed entity — a generated parameter sweep, a rate table, a component registry, anything shaped as key->value rather than free text (an artifact) or a Petri net (a model). Returns its content id; the same data, even with keys inserted in a different order, returns the same id.
| Name | Required | Description | Default |
|---|---|---|---|
| data | Yes | the table as a JSON object |
Changes observed during successful MCP inspections.
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Beyond the annotations, the description adds meaningful behavior: content-addressed identity, order-insensitive key insertion, and the fact that the return value is a content id. This enriches the agent's understanding without contradicting the provided annotation hints.
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?
Two sentences with the core action front-loaded, followed by clarifying examples and the content-addressing identity behavior. Every clause earns its place; there is no filler.
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 one-parameter store operation, the description covers what is stored, what is returned (content id), and the deduplication behavior. Annotations cover side-effect hints, and no output schema is needed given the simple return value.
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 already documents the single data parameter as 'the table as a JSON object,' so the baseline is 3. The description adds semantic value by framing it as a key->value lookup table and giving concrete examples such as parameter sweeps, rate tables, and component registries.
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-resource pair: 'Store a key->value lookup table as its own content-addressed entity.' It further distinguishes itself from free-text artifacts and Petri-net models, making its purpose unmistakable and differentiating it from sibling tools.
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 gives a clear decision rule: use this for anything shaped as key->value rather than free text or a Petri net. It doesn't explicitly name retrieval siblings like sim_map_get or sim_map_list, but for a store operation the context is sufficient.
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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