Poll for incoming work by checking if anyone has called your registered functions. Returns calls targeting your AIP address as the provider.
Check if anyone has called your registered functions. Returns function calls where your address is the target provider. Use this to poll for incoming work.
| Name | Required | Description | Default |
|---|---|---|---|
| my_address | Yes | Your AIP address (from identity.json). |
Changes observed during successful MCP inspections.
v0.7.0Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description bears full responsibility for behavioral disclosure. It does not state whether this is a read-only operation, whether it changes the state of the returned calls (e.g., marks them as read), or what side effects, if any, polling has. It simply says it 'returns function calls,' which is insufficient for an agent to understand the tool's runtime behavior.
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?
The description is two short sentences with no filler. The action is front-loaded ('Check if anyone has called your registered functions'), followed by a clarifying elaboration on return filtering, and ends with a direct usage instruction. Every word 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?
The tool is simple (one parameter, no nested objects), but there is no output schema, and the description does not describe the shape of the returned function calls, nor the behavior when there are no calls, nor whether repeated polling re-returns the same calls. These are meaningful gaps for an agent that must consume the result. It is adequate but incomplete.
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 100% for the single parameter, and the schema already explains 'Your AIP address (from identity.json).' The description adds some context by indicating the address is used to identify calls where you are the target provider, which slightly enriches the parameter's meaning. Baseline 3 is appropriate because the schema does the heavy lifting and the description adds only marginal semantic value.
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 states a specific action ('Check if anyone has called your registered functions') and a resource ('function calls where your address is the target provider'). It clearly distinguishes the tool from likely siblings like peck_function_call (making a call) and peck_function_register (registering a function), but it does not explicitly name or contrast with those alternatives, so it falls just short of a 5.
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 provides clear usage context: 'Use this to poll for incoming work.' This tells the agent when to invoke the tool. However, it offers no exclusions or explicit mention of alternatives, so an agent might still be uncertain when to choose this versus other function-related tools.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.