generect-mcp

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

Annotations are absent, so the description carries full disclosure burden. While it identifies the external service (Generect), it fails to clarify if emails are guesses/patterns or verified, whether the operation is idempotent, rate limits, or critical for a generation tool lacking output schema what data structure is returned.

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?

Single sentence that is appropriately front-loaded. Minor inefficiency in 'via Generect Email Generator' (repetition of 'email') and slightly awkward preposition 'by' instead of 'from' keep it from a 5, but generally tight and readable.

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 100% input schema coverage, the input side is well handled. However, given no output schema and no annotations, the description should compensate by describing return values or success/failure modes. It mentions the external service but leaves critical gaps in explaining what the agent receives back.

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 coverage is 100%, so the schema fully documents all four parameters including timeout_ms. The description reinforces the key inputs (first/last name, domain) but doesn't add semantic value beyond what the schema already provides, warranting the baseline score.

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 generates emails using first/last name and domain via a specific service (Generect). It distinguishes from siblings like search_leads and get_lead_by_url which retrieve existing data, while this creates new email addresses. Minor ambiguity around 'generate' (compose vs construct address) prevents 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?

No explicit guidance on when to use this versus searching existing leads (search_leads) or retrieving verified records. No mention of prerequisites like valid domain format or when to use the optional timeout_ms parameter compared to default behavior.

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 provided, the description carries the full burden of behavioral disclosure but fails to explain what data constitutes a 'Lead', default inclusion behavior for the optional data flags (posts/comments), error handling for invalid URLs, or rate limiting.

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 four-word description is front-loaded and contains no fluff, but it is undersized for a 6-parameter tool with complex behavioral flags, leaving significant gaps that require elaboration on usage patterns.

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 6 parameters controlling data inclusion and no output schema, the description lacks necessary context about return structure, default behaviors for optional fields, and how the boolean flags affect the response payload.

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%, establishing a baseline score of 3. The description adds no supplemental context for the boolean flags (e.g., performance implications of including posts) or timeout parameter, but the schema adequately documents each parameter's basic purpose.

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 'Get Lead by LinkedIn URL' is tautological, essentially restating the tool name. While it identifies the action (Get) and resource (Lead), it fails to specify the scope of data returned or distinguish this direct lookup from the sibling search_leads tool.

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?

No guidance is provided on when to use this specific lookup versus the sibling search_leads tool, nor are prerequisites (like URL format requirements) or optimal use cases 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?

With no annotations, the description carries the full burden of behavioral disclosure. It mentions the request is 'quick' but fails to explain what constitutes success/failure, what the response contains (status code vs. lead data), or whether it consumes API quota. The mention of 'lead-by-link' creates ambiguity 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 sentence that front-loads the primary purpose. It is appropriately brief, though the phrasing 'Health check... via' is slightly awkward. No wasted words, but could benefit from a second sentence explaining the output.

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 health check tool with no output schema or annotations, the description inadequately explains what the tool returns (health status object vs. actual lead data) or how to interpret the results. Given the sibling tool 'get_lead_by_url', the ambiguity about whether this returns lead data constitutes a significant 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?

With 100% schema coverage, the schema adequately documents both parameters. The description adds the phrase 'lead-by-link' which loosely connects to the 'url' parameter's purpose, but doesn't add syntax details, format constraints, or usage examples beyond what the schema already provides. Baseline 3 is appropriate given complete 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 specifies the action ('Health check') and target ('Generect API'), and mentions the mechanism ('lead-by-link request'). However, it doesn't clearly distinguish this from the sibling tool 'get_lead_by_url', leaving ambiguity about whether this returns actual lead data or just a status check.

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?

No guidance provided on when to use this tool versus alternatives. It should specify to use this for API connectivity verification before calling other tools, and clarify if it should not be used for actual lead retrieval (which likely requires 'get_lead_by_url').

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 should disclose behavioral traits. It only says 'Search', but does not mention pagination, error handling, rate limits, or what happens with fallback parameters like 'fallback_from_leads'. The tool's behavior is largely opaque.

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, concise sentence with no wasted words. It is front-loaded with the key action and resource, achieving maximum efficiency.

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 (11 parameters, no output schema, sibling tools), the description is far too minimal. It fails to explain return values, filter combinations, or how 'ICP' relates to the parameters, leaving the agent underinformed.

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% coverage with parameter descriptions, so the schema does the heavy lifting. The tool description adds no additional meaning beyond what is already in the schema, meeting the baseline expectation.

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 'Search for companies by ICP filters', specifying the action (search) and resource (companies). However, it does not differentiate from sibling tool 'search_leads', nor explain what 'ICP filters' means, leaving some 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?

No guidance is provided on when to use this tool versus alternatives like 'search_leads'. There is no mention of prerequisites, ideal use cases, or conditions where this tool should not be used.

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 full burden. It fails to disclose pagination behavior, result format, or side effects, leaving critical behavioral traits undocumented.

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 with no fluff. However, it could be more informative without losing conciseness.

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 11 parameters, no output schema, and no annotations, the description is inadequate. It omits important details like default pagination, required parameters (none, but still), and expected return format.

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 baseline is 3. The description adds no extra meaning beyond the schema, but does not detract either.

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 searches for leads using ICP filters, which is a specific verb and resource. However, it does not differentiate from sibling tools like get_lead_by_url or search_companies, and 'ICP' is undefined but likely understood.

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?

No guidance on when to use this tool versus alternatives. No exclusions or context provided, leaving the agent without decision support.

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