Hemrock MCP

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

Annotations indicate 'readOnlyHint: true' and 'openWorldHint: false', which the description does not contradict. The description adds value by specifying the content type ('financial modeling best practices and design principles') and use case ('background context for AI interactions'), but it does not disclose additional behavioral traits like rate limits, authentication needs, or response format. With annotations covering safety and scope, a 3 is appropriate as the description adds some context without rich behavioral details.

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 well-structured: two sentences that efficiently convey the tool's purpose and usage. The first sentence states what the tool does, and the second provides context without redundancy. Every sentence earns its place, making it front-loaded and zero waste.

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 low complexity (1 optional parameter, no output schema) and rich annotations (readOnlyHint, openWorldHint), the description is reasonably complete. It covers the purpose and usage context, though it could benefit from more detail on output format or limitations. The annotations help fill gaps, but the description does not fully explain return values or error handling, slightly reducing completeness.

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 100% description coverage, with the 'topic' parameter fully documented in the schema (including enum values and optionality). The description mentions 'for a given topic' but does not add semantic details beyond what the schema provides, such as examples or edge cases. With high schema coverage, the baseline score is 3.

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: 'Returns Hemrock's financial modeling best practices and design principles for a given topic.' It specifies the verb ('Returns'), resource ('Hemrock's financial modeling best practices and design principles'), and scope ('for a given topic'). However, it does not explicitly differentiate from sibling tools like 'get_checks', 'get_context', or 'get_prompts', which prevents a score of 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 some usage guidance: 'Useful as background context for AI interactions.' This implies the tool is for informational purposes, but it does not specify when to use this tool versus alternatives (e.g., 'get_context' or 'get_prompts'), nor does it mention exclusions or prerequisites. The guidance is implied rather than explicit.

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 annotations already declare readOnlyHint=true and openWorldHint=false, indicating this is a safe read operation with closed-world behavior. The description adds valuable context about what the tool returns ('where AI gets financial modeling wrong' guidance) and its purpose (auditing/catching errors), which goes beyond what annotations provide. No contradictions exist between description and 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 just two sentences that each serve distinct purposes: the first states what the tool returns and its nature, the second provides clear usage guidance. Every word earns its place with zero wasted text.

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 moderate complexity (single parameter, read-only operation) and the absence of an output schema, the description provides good contextual completeness. It explains what the tool returns and when to use it, though it doesn't detail the format or structure of the returned validation checks. The annotations cover safety aspects well.

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 100% schema description coverage, the input schema already fully documents the single parameter with its enum values and description. The tool description doesn't add any additional parameter semantics beyond what's in the schema, so it meets the baseline expectation without providing extra 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 clearly states the tool's purpose with specific verbs ('returns', 'audit', 'catch') and resources ('Layer 3 sanity-check and validation prompts', 'AI-generated work', 'common modeling errors'). It distinguishes itself from sibling tools like get_best_practices, get_context, and get_prompts by focusing specifically on validation checks for financial modeling errors.

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: 'to audit AI-generated work or catch common modeling errors.' It provides clear context about the tool's intended application in financial modeling validation scenarios, though it doesn't explicitly mention when not to use it or name specific alternatives among siblings.

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?

Annotations already declare readOnlyHint=true and openWorldHint=false, indicating it's a safe read operation with closed-world behavior. The description adds valuable context about the tool's role as a prerequisite that should be run first, which goes beyond what annotations provide. However, it doesn't describe rate limits, authentication needs, or detailed behavioral traits.

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 with just two sentences that are front-loaded with essential information. Every sentence earns its place by stating the tool's purpose and providing critical usage guidance without any wasted words.

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 moderate complexity (single optional parameter, no output schema), the description is reasonably complete. It explains the tool's purpose, usage guidelines, and parameter effect. However, without an output schema, it could benefit from mentioning what the return value contains or its format, though this is partially compensated by the clear purpose statement.

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 fully documents the single optional parameter with its enum values. The description mentions the parameter's effect ('appends a template-specific primer') but doesn't add significant meaning beyond what the schema already provides. Baseline 3 is appropriate when schema does the heavy lifting.

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 specific action ('Returns') and resource ('universal context-setting primer for Hemrock models'), and distinguishes it from siblings by emphasizing it should always be run first before any other prompts. This provides a clear, specific purpose with sibling differentiation.

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 ('Always run this first before any other prompts'), providing clear usage context and distinguishing it from alternatives by positioning it as a prerequisite. This gives explicit guidance on when to use it versus other 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?

Annotations indicate readOnlyHint=true and openWorldHint=false, so the agent knows this is a safe, read-only operation with a closed set of inputs. The description adds context by specifying that the prompts are 'ready-to-use' and for 'common modeling tasks', which provides additional behavioral insight beyond the annotations. However, it does not disclose details like rate limits, authentication needs, or error handling.

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, consisting of a single sentence that directly states the tool's function and key attributes ('ready-to-use prompts for common modeling tasks'). There is no wasted text, and every part of the sentence contributes essential 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 tool's low complexity (2 parameters, 1 required), high schema coverage (100%), and annotations providing safety and input constraints, the description is mostly complete. It effectively explains the tool's purpose and usage context. However, without an output schema, it could benefit from mentioning the return format (e.g., list of prompts), but this is a minor gap given the overall 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?

Schema description coverage is 100%, with clear descriptions for both parameters (e.g., 'Optional task type filter' and 'The Hemrock template being used' with an enum). The description adds value by explaining that these parameters filter 'task-specific Layer 2 prompts', but it does not provide additional semantic details beyond what the schema already covers, such as examples or usage nuances.

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 a specific verb ('Returns') and resource ('task-specific Layer 2 prompts'), and specifies the domain ('Hemrock task prompts'). However, it does not explicitly differentiate from sibling tools like get_best_practices or get_checks, which might also retrieve related data but for different resources.

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 implies usage for 'common modeling tasks' and mentions filtering by template and task type, but it does not provide explicit guidance on when to use this tool versus alternatives (e.g., get_best_practices) or any exclusions. The context is clear but lacks sibling differentiation or specific when-not-to-use scenarios.

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