CodeReclaimers Capability Oracle

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

Annotations cover key behavioral traits (read-only, non-destructive, idempotent, closed-world), so the bar is lower. The description adds useful context about the tool's intended use case ('fast scheduling checks') and scope ('Alan McIntyre (CodeReclaimers LLC)'), which goes beyond annotations. No contradiction with annotations exists.

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 sentences, front-loaded with the core purpose and followed by usage guidance. Every sentence adds value without redundancy, making it efficient and well-structured.

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?

Given the tool's simplicity (0 parameters, no output schema) and rich annotations, the description is complete enough for its purpose. It explains what the tool does and when to use it, though it could optionally mention the lack of parameters or expected output format for full clarity.

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 input schema has 0 parameters with 100% coverage, so the baseline is 4. The description does not need to add parameter information, and it appropriately focuses on the tool's purpose and usage instead.

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 tool's purpose with specific verb ('Returns') and resource ('current availability status for Alan McIntyre (CodeReclaimers LLC)'). It distinguishes itself from sibling tools by specifying it's for 'fast scheduling checks before a full capability query,' differentiating from data.manifest and query.capabilities.

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 explicit usage guidance: 'Use for fast scheduling checks before a full capability query.' This indicates when to use this tool (fast checks) versus alternatives (full capability query), with query.capabilities likely being the alternative 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?

The description adds valuable context beyond annotations by specifying what data is returned (domains, engagement types, project list, endpoint URLs) and the use case (systematic filtering). While annotations already declare readOnlyHint=true, idempotentHint=true, and destructiveHint=false, the description provides meaningful behavioral information about the tool's output content and purpose that isn't captured in the structured annotations.

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 perfectly concise with two sentences that each earn their place. The first sentence explains what the tool returns, and the second sentence explains when to use it. There's zero waste or redundancy, and the information is front-loaded with the core functionality.

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?

Given the tool's complexity (parameterless read operation), rich annotations (readOnlyHint, idempotentHint, etc.), and lack of output schema, the description provides excellent context about what data is returned and the use case. It could potentially mention the return format or structure more explicitly since there's no output schema, but it's still highly complete for this type of tool.

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?

With 0 parameters and 100% schema description coverage, the baseline would be 4. The description appropriately doesn't discuss parameters since none exist, and instead focuses on what the tool returns and its use case, which is the correct emphasis for a parameterless tool.

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 tool's purpose with specific verbs ('Returns') and resources ('full structured capability manifest for Alan McIntyre'), including detailed content elements (domains, engagement types, project list, endpoint URLs). It distinguishes from siblings by specifying this is for systematic filtering across consultant candidates, while data.availability and query.capabilities likely serve different purposes.

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 explicitly states when to use this tool: 'Use for systematic filtering across multiple consultant candidates.' This provides clear context for application and distinguishes it from potential alternatives that might not provide the same comprehensive manifest data for comparison purposes.

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?

The description adds valuable context beyond annotations: it specifies the knowledge base is about Alan McIntyre's capabilities and clarifies what it does not do (make commitments, schedule, negotiate). Annotations already cover read-only, open-world, idempotent, and non-destructive aspects, so the description appropriately supplements with domain-specific behavioral constraints.

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 efficiently structured in three sentences: first states purpose and scope, second clarifies limitations, third provides usage guidance. Every sentence adds value without redundancy, making it front-loaded and concise.

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?

Given the tool's complexity (simple query with one parameter), rich annotations, and no output schema, the description is mostly complete. It covers purpose, usage, and limitations well, but could slightly enhance completeness by mentioning response format or example question types, though annotations help mitigate this gap.

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%, with the parameter 'question' fully documented in the schema. The description does not add any additional parameter details beyond what the schema provides, such as examples or formatting tips, so it meets the baseline for high schema coverage.

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 tool's purpose: 'Query the knowledge base of Alan McIntyre' with specific domains (computational geometry, scientific C++/Python/Julia computing, neuroevolution) and what it returns (factual answers about skills, project history, general availability). It distinguishes from siblings by specifying it doesn't make commitments or schedule engagements.

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 explicitly states when to use this tool: 'Use this tool when assessing consultant fit for a technical project in these domains.' It also provides clear exclusions: 'Does not make commitments, schedule engagements, or negotiate terms,' which helps differentiate from potential alternatives.

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