accord_trace_hash
Compute AccordTrace canonical SHA-256 for JSON-compatible data without creating a proof.
Input Schema
| Name | Required | Description | Default |
|---|---|---|---|
| data | Yes |
Compute AccordTrace canonical SHA-256 for JSON-compatible data without creating a proof.
| Name | Required | Description | Default |
|---|---|---|---|
| data | Yes |
Changes observed during successful MCP inspections.
Input schema / additionalPropertiesRemoved value: -falseInput schema / properties / data / descriptionRemoved value: -"Exact string or JSON value to hash."Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the behavioral burden. It discloses that the tool computes a canonical SHA-256 and does not create a proof, which suggests a pure read-only computation. However, it does not mention side effects, return format, input limitations, or whether data is transmitted/stored, so behavioral context is only partial.
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?
One tight sentence with no filler. The core action, input scope, and key exclusion are front-loaded, and every clause 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 one-parameter hashing tool, the description covers the main input requirement and the key behavioral distinction. However, there is no output schema and the description does not state the return value (e.g., hex-encoded digest), which the agent would need to interpret the result. This keeps it from being fully complete.
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 single 'data' parameter has no schema-level description. The description compensates partially by specifying 'JSON-compatible data', which clarifies the expected input type. However, it does not explain canonicalization rules or what shapes of JSON are accepted, so it does not fully cover the semantic gap.
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 operation ('Compute AccordTrace canonical SHA-256') and a clear input class ('JSON-compatible data'). The phrase 'without creating a proof' distinguishes it directly from the sibling accord_trace_create_proof, so an agent can tell them apart.
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 conveys when to use the tool: when only a canonical hash is needed, not a proof. It implicitly excludes proof-creation use cases via 'without creating a proof', but it does not explicitly name sibling tools or provide a conditional routing rule, leaving a small gap in guidance.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Add one secure layer between your agents and this server.