get_zk_provenance
Return cryptographic provenance proof for a record ID from current datasets.
Input Schema
TableJSON Schema
| Name | Required | Description | Default |
|---|---|---|---|
| record_id | Yes | Record ID to get provenance for |
Kairos Signal — 63-Layer Symplectic Neural ODE
Schema Changelog
Changes observed during successful MCP inspections. Dates show when Glama detected each change.
Changed3 schema fields changed
- removed
Input schema / properties / lead_idRemoved value: -{ - "description": "Lead ID to get provenance for", - "type": "integer" -} - added
Input schema / properties / record_idAdded value: +{ + "description": "Record ID to get provenance for", + "type": "integer" +} - changed
Input schema / requiredPrevious value: -[ - "lead_id" -]New value: +[ + "record_id" +]
- First observed
TDQS
A3.5/5.0
Behavior3/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
The description discloses that the tool generates a cryptographic proof, implying a read operation, and scopes it to current datasets. However, it does not describe error behavior, whether any authentication is implicitly required, or the format of the returned proof. Since no annotations are present, the description carries the full burden and provides only basic information.
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 sentence that is concise and front-loaded with the primary action and object. It contains no redundant information, achieving high efficiency.
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 with one parameter and no output schema, so the description's one-sentence definition is partially adequate. However, without annotations or output schema, the description does not help the agent understand response structure or failure modes, leaving some gaps for a complete mental model.
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 schema description for record_id is straightforward and covers 100% of the parameter, so the tool description adds no additional semantic value. The description in the tool text does not elaborate on the record_id format or constraints beyond the schema.
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 'return' and identifies the resource as 'cryptographic provenance proof for a record ID', clearly distinguishing it from sibling tools like verify_footprint. The scope 'from current datasets' adds useful context. No sibling tool mentions provenance, so this uniquely identifies the tool's purpose.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
There is no guidance on when to use this tool versus alternatives such as verify_footprint or fetch_dataset. The description does not mention any prerequisites, exclusions, or typical use cases. This is a significant gap given the related sibling tools.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.