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ai_provenance

Full cryptographic provenance chain for evidence data. EU AI Act explainability.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
symbolYesToken symbol
frameworksNo

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observed

TDQS

C2.6/5.0
Behavior1/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

With no annotations, the description carries full burden for behavioral disclosure. It only says the chain is 'cryptographic' and 'full', providing no details about whether this is a read operation, what the output structure is, whether it verifies hashes, or any side effects. This is a minimal functional statement, not transparency.

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

Conciseness4/5

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

The description is two short sentences, front-loaded with the core concept. The second sentence is a fragment but adds regulatory context. While concise, it lacks any structural detail (e.g., return value, use cases) that would be expected for a cryptographic tool.

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

Completeness1/5

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

There is no output schema and no annotations, so the description must explain what the provenance chain actually provides. It omits the content of the chain, whether it verifies or just retrieves, how to interpret the result, and any specifics of the EU AI Act requirement. This is insufficient for a tool with even moderate complexity.

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

Parameters2/5

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

Schema coverage is only 50% (symbol is described, frameworks is not), and the description adds no information about either parameter. It fails to explain what 'symbol' refers to or what 'frameworks' does, leaving half the parameters semantically unclear. The description does not compensate for the schema's gaps.

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

Purpose4/5

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

The description names a specific resource ('cryptographic provenance chain for evidence data') and indicates scope (EU AI Act explainability), which is fairly clear. However, it lacks a verb and does not strongly distinguish itself from sibling tools like audit_query or evidence_profile, which could also deal with provenance.

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

Usage Guidelines3/5

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

The mention of 'EU AI Act explainability' implies a compliance-oriented use case, but there is no explicit guidance on when to use this tool versus alternatives like ai_explain or audit_query. It does not state exclusions or preferred conditions, leaving usage context under-specified.

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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