Hive Compute Grid

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

With no annotations provided, the description carries the full burden of behavioral disclosure. It explicitly declares 'read-only' and 'pure read-only telemetry', which is a key behavioral trait. It also enumerates excluded functionality, but doesn't disclose potential aspects like data freshness or error behavior. Still, for a simple no-param read, this is adequate and exceeds minimal expectations.

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 a single sentence that front-loads the core purpose ('Read-only capacity view') and then adds necessary exclusions. Every word contributes value; there is no fluff or redundancy. The spec reference adds authority without bulk.

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 zero-parameter tool with no output schema and no annotations, the description is remarkably complete. It states purpose, safety profile (read-only), and scope (what it excludes). The only minor omission is a precise definition of 'capacity,' but the core usage context is fully covered.

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?

There are zero parameters, so the baseline is 4. The description adds no parameter-specific detail, but there is nothing to document. The schema is empty, and the description's scope statement is sufficient.

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 clearly states a specific verb and resource: 'Read-only capacity view from the Capacity Listener fleet.' It also explicitly enumerates what the tool does NOT do ('NO bids, NO hedges, NO positions, NO derivatives'), which distinguishes it from siblings like list_agents and verify_proof.

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 context about the tool's scope (pure read-only telemetry) and what it excludes, which implicitly signals when to use it. However, it does not explicitly name alternative tools or state conditions like 'use this when you need capacity data, not trading details.' The exclusions help but no direct when-to-use comparison is given.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

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

No annotations are provided, so the description carries the full burden. It explicitly states 'No auth required' and enumerates the return fields (agent type, count, revenue model), giving the agent a clear picture of the tool's behavior. It does not describe error conditions or further side effects, but for a simple read-only list, this is adequate.

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?

Two concise sentences, front-loaded with the verb and resource. Every word adds value; no fluff or redundancy.

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 zero-parameter list tool with no output schema, the description covers return fields, scope, and auth requirements. It is sufficiently complete for an agent to understand what the tool does and what output to expect.

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?

The tool has zero parameters, so there is nothing to explain. The description adds context by specifying the fleet size (15 agents) and driver type diversity (6 driver types), which is valuable even though the schema is empty.

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 uses a specific verb ('List') and clearly identifies the resource ('15-agent compute grid fleet across all 6 driver types'). It also states the return fields, making it unambiguous and distinct from sibling tools like computegrid_get_capacity and computergrid_verify_proof.

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 clearly implies its use for listing the fleet and details the returned data. However, it does not explicitly compare with sibling tools or state when not to use it. Sibling names suggest different purposes, but no explicit alternatives are mentioned.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

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

No annotations are provided, so the description carries the full burden. It mentions the fleet size and cost, but omits critical behavioral details: whether the operation is synchronous, what happens on verification failure, required permissions/auth, and any side effects. For a submission/payment-triggering action, this is a significant gap.

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 extremely concise: three short sentences that state the action, return value, and cost. Every sentence earns its place, with no filler or redundancy.

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?

With no annotations or output schema, the description is the only source of behavioral context. It fails to explain error handling, response format beyond 'proof', whether the job submission is asynchronous, or any prerequisites. This is incomplete for a tool that initiates a paid verification process.

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%, so the schema already documents each parameter. The description adds no parameter-specific meaning, but it doesn't need to since the schema is complete. Baseline 3 is appropriate.

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 clearly states the action ('Submit a compute job for verification') and the resource (Verification Fleet), and notes the return value (Groth16-style proof). It distinguishes from siblings (capacity/agents) by focusing on verification. However, the tool name 'verify_proof' suggests verifying a proof, while the description says submitting a job, which creates minor ambiguity.

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?

Usage is implied: use this when you need to verify a compute job and obtain a proof. However, there is no explicit guidance on when not to use it or alternatives, and sibling tools are not mentioned.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.