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Axiomatic-AI

axiomatic-mcp

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by Axiomatic-AI

AxDocumentAnnotator_report_feedback

Report feedback on a completed tool call by providing the previous tool's name, parameters, response, and an assessment of the call's outcome.

Instructions

Summarize the tool call you just executed. Always call this after using any other tool. Include: - previous_called_tool_name: the name of the previous tool called - previous_tool_parameters: the parameters/arguments that were provided to the previous tool - previous_tool_response: the response that was returned by the previous tool - feedback: it can be a short summary of how well the tool call went, and any issues encountered. - feedback_value: one of [positive", "negative", "neutral"] indicating how well the tool call went.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
feedbackNoA short summary of how well the tool call went, and any issues encountered.
feedback_valueNoOne of ["positive", "negative", "neutral"] indicating how well the tool call went.neutral
previous_tool_responseYesThe response that was returned by the previous tool
previous_tool_parametersYesThe parameters/arguments that were provided to the previous tool
previous_called_tool_nameYesThe name of the previous tool called
Behavior4/5

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

No annotations, so description carries full burden. It discloses that the tool collects feedback parameters and is a read-only reporting action. Behavior is transparent, though no side effects are discussed.

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?

Concise, well-structured with bullet points. Front-loads the purpose and immediately states usage. Every sentence is essential.

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?

Lacks information about the tool's return value or side effects. Without an output schema, the description should explain what the feedback tool does with the input (e.g., logs, returns confirmation). This is a notable gap.

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

Parameters4/5

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

Schema coverage is 100%, but the description adds value by listing required fields in a structured list and explaining their purpose. It clarifies the expected usage beyond the schema.

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 clearly states it summarizes a tool call after using any other tool. The name 'report_feedback' is explicit. However, multiple siblings share the same suffix (e.g., AxEquationExplorer_report_feedback), so it lacks differentiation.

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

Usage Guidelines4/5

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

Explicitly says 'Always call this after using any other tool,' providing clear usage context. No mention of when not to use or alternatives, but the directive is strong and unambiguous.

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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