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 states that the tool maps symptoms to 'recommended playbooks and example investigation queries', implying a read-only recommendation operation without executing queries. This adds useful context about what the tool does and does not do, though it does not explicitly state 'read-only' or describe any side effects.

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, information-dense sentence that front-loads the main action ('Map symptom keywords') and then provides specific examples. Every word earns its place with no redundancy or fluff, making it highly 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?

For a simple 2-parameter tool without an output schema, the description is adequately complete. It explains the core mapping behavior and gives example inputs, and the 'limit' parameter implies a list of solutions. However, it does not detail the return structure (e.g., how playbooks or queries are represented), which leaves a minor gap. Overall, it is sufficient for the tool's complexity.

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 already documents both parameters with 100% coverage, providing a baseline of 3. The description adds value by enumerating valid symptom keywords (e.g., 'slow ingestion', 'partition churn', 'memory pressure'), which gives concrete guidance beyond the schema's generic 'symptom phrases'. This extra context helps the agent form correct input values.

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 ('Map') and clearly identifies the input (symptom keywords) and output (playbooks and example investigation queries). It lists concrete examples like 'slow ingestion' and 'replication lag', making the purpose unambiguous. This differentiates it from siblings like search_solutions and health_check, which focus on searching or health status rather than symptom-to-playbook mapping.

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 when to use the tool: when the user has symptoms such as slow ingestion, partition churn, or query latency. It provides a clear context for use but does not explicitly exclude alternatives or mention sibling tools, so it stops short of a full 5.

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 burden of behavioral disclosure. It indicates a read operation ('get') and reveals that IDs and aliases are supported, but it does not mention potential errors, permissions, or the structure of the full playbook entry. This is acceptable for a simple retrieval tool but lacks depth.

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 concise and front-loaded with the action ('Get a full playbook entry'), followed by useful examples. Both sentences earn their place with no filler or repetition of schema information.

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 simplicity of a single required parameter and no output schema, the description provides enough context for an agent to understand what the tool does and how to invoke it. It could be more explicit about the return structure or error handling, but for this scope, it is reasonably complete.

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 schema covers the id parameter at 100%, and the description adds valuable semantic details by providing concrete example values (too-many-parts, ingestion-batching) and explaining that aliases like write-amplification are accepted. This goes beyond the schema's generic 'Solution id or alias' description.

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 retrieves a full playbook entry by id, with specific examples (too-many-parts, ingestion-batching). It distinguishes the tool from siblings (search_solutions, diagnose_symptoms, health_check) by focusing on retrieval by unique identifier.

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 implicitly conveys when to use this tool: when you have an id or alias. The mention of 'from search_solutions or playbook index' in the schema complements the description, but the description itself does not explicitly contrast with search_solutions for discovery use cases. However, the examples and 'by id' phrasing give clear contextual usage.

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?

With no annotations, the description carries the burden of disclosing behavior. It states the tool reports status (read-only) and requires no external services, which is useful context. It doesn't detail return format or side effects, but the nature of a health check makes destructive behavior unlikely.

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, focused sentence that conveys all necessary information without redundancy. Every word earns its place, and it is front-loaded with the primary action.

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 simple zero-parameter health check tool with no output schema and clear sibling distinctions, the description is complete. It states what the tool reports and that no external services are required, which is sufficient for an agent to select and invoke it correctly.

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 the description doesn't need to explain parameter meanings. The schema is empty, and the description adds value by indicating the output content (entry count, mode), which complements the schema.

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 ('Report') and resource ('Cimon-MCP playbook server status') with concrete details (entry count, mode). This clearly distinguishes it from sibling tools like search_solutions, get_solution, and diagnose_symptoms, which 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 phrase 'No external services required' provides clear usage context, implying this is a lightweight, dependency-free status check. While it doesn't explicitly exclude alternatives, the purpose is self-evident and distinct from the sibling tools.

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 must convey safety and behavior. It discloses that the tool works offline without a database, which is useful, but fails to state whether it is read-only, what it returns, or any other behavioral traits. This leaves the agent uncertain about side effects.

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 27-word sentence, front-loaded with the action and resource, followed by a useful offline note. 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?

With no output schema and only 2 params, the description is fairly complete but does not explicitly state the return value (e.g., a list of playbooks) or how the limit parameter affects results. This is a minor gap for a simple search tool, making it adequate but not outstanding.

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 schema descriptions fully cover both parameters (query and limit), so the description does not need to add parameter details. It adds no extra meaning beyond the schema's own descriptions.

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 the specific verb 'Search' with a clear resource ('curated playbooks') and enumerates relevant topics (ingestion batching, write amplification, etc.). It clearly differentiates from siblings like get_solution and diagnose_symptoms by focusing on search over a curated library.

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?

It provides context for use (searching playbooks for high-volume stream problems) but does not explicitly name alternatives or exclusion criteria. The 'Works offline' note hints at when to use (no DB required), but lacks direct comparison with sibling tools.

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