Generate hyper-personalized cold email sequences via MachFive API.
Download the generated email sequences for a COMPLETED list.
Only call this AFTER get_list_status shows processing_status = 'completed'. If the list is not yet completed, you'll get a 409 error — poll first.
| Name | Required | Description | Default |
|---|---|---|---|
| format | No | Output format: 'json' (structured data) or 'csv' (raw CSV for sending tools). | json |
| list_id | Yes | List UUID to export. |
| Name | Required | Description |
|---|---|---|
| result | Yes |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint=true and destructiveHint=false, so the agent knows it's a safe read. The description adds valuable behavioral context beyond annotations: the dependency on prior processing status and the specific 409 error behavior. This exceeds baseline transparency.
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: the first states purpose, the second gives a critical usage condition and expected error. No filler or redundancy; information is front-loaded and directly useful.
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, strong annotations, and presence of an output schema, the description sufficiently covers the key operational constraint (completion prerequisite) and error handling. It is complete for an agent to decide when and how to invoke this tool reliably.
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%, with both parameters (list_id and format) having meaningful descriptions. The description does not add parameter-specific details beyond the schema, but it does subtly reinforce that the export is for completed lists. Thus, it meets the baseline for high schema coverage without adding extra semantic 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 uses a specific verb ('Download') and a clear resource ('generated email sequences for a COMPLETED list'). It distinguishes itself from sibling tools like generate_batch, generate_sequence, and get_list_status by focusing on exporting results rather than creating or monitoring.
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?
Provides explicit when-to-use guidance: only after get_list_status shows processing_status = 'completed'. It also explains the failure mode (409 error) and instructs to poll first, which is highly actionable for an agent.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Submit multiple leads for batch email sequence generation (ASYNC).
Returns IMMEDIATELY with a list_id. Processing runs in the background. After calling this, poll get_list_status every 15-30 seconds until processing_status is 'completed' or 'failed', then call export_list.
You must have a campaign_id first. Call list_campaigns if you don't have one.
| Name | Required | Description | Default |
|---|---|---|---|
| list_name | No | Display name for this batch in MachFive UI. | |
| leads_json | Yes | JSON array of lead objects. Each MUST have "email". Optional: name, title, company, company_website, linkedin_url. Example: '[{"email":"jane@acme.com","name":"Jane Doe"}]' | |
| campaign_id | Yes | Campaign UUID from list_campaigns. | |
| email_count | No | Number of emails per lead, 1-5. | |
| approved_ctas | No | Comma-separated CTAs. Omit to use campaign defaults. | |
| campaign_angle | No | Additional context/angle for personalization. | |
| email_signature | No | Signature appended to each email. |
| Name | Required | Description |
|---|---|---|
| result | Yes |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
The description discloses critical asynchronous behavior beyond the annotations: 'Returns IMMEDIATELY with a list_id. Processing runs in the background.' It also explains the polling pattern with get_list_status and the eventual export_list call. This is valuable context not captured in the annotations or schema.
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 compact and front-loaded, with the key behavior ('ASYNC', 'Returns IMMEDIATELY') stated upfront. It delivers the workflow in three sentences without 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?
Given the tool's async nature and the need for follow-up steps, the description covers all critical context: prerequisite, immediate return, background processing, polling, and final export. The output schema is not shown, but the description explicitly mentions the returned list_id, so the agent has the necessary information to use the tool 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 schema has 100% parameter description coverage, so the baseline is 3. The description adds minimal parameter-specific detail beyond the schema, only mentioning the need for a campaign_id first. It doesn't enrich the schema's existing parameter 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 clearly states the tool's function: 'Submit multiple leads for batch email sequence generation (ASYNC).' It uses a specific verb and resource, and the ASYNC qualifier distinguishes it from synchronous operations. Sibling tools like generate_sequence are implicitly differentiated by the 'batch' focus.
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: it starts with the prerequisite 'You must have a campaign_id first' and tells users to call list_campaigns if needed. It also outlines the post-call workflow: poll get_list_status, then call export_list. This clearly indicates when and how to use the tool relative to its siblings.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Generate a personalized cold email sequence for ONE lead.
This is SYNCHRONOUS — the request takes 3-10 minutes because MachFive researches the prospect and crafts unique emails. Do NOT retry if it seems slow; wait for the response.
You must have a campaign_id first. Call list_campaigns if you don't have one. If the request times out, use the returned list_id with get_list_status and export_list to recover results.
| Name | Required | Description | Default |
|---|---|---|---|
| name | No | Lead's full name (improves personalization). | |
| Yes | Lead's email address (REQUIRED). | ||
| title | No | Lead's job title (improves personalization). | |
| company | No | Lead's company name (improves personalization). | |
| list_name | No | Display name for this list in MachFive UI. | |
| campaign_id | Yes | Campaign UUID from list_campaigns. | |
| email_count | No | Number of emails in sequence, 1-5. | |
| linkedin_url | No | LinkedIn profile URL for deeper personalization. | |
| approved_ctas | No | Comma-separated CTAs, e.g. 'Direct Meeting CTA, Lead Magnet CTA'. Omit to use campaign defaults. | |
| campaign_angle | No | Additional context/angle for personalization. | |
| company_website | No | Company URL for AI research. | |
| email_signature | No | Signature appended to each email. |
| Name | Required | Description |
|---|---|---|
| result | Yes |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
The description discloses synchronous execution taking 3-10 minutes, warns against retrying, and explains how to recover results if a timeout occurs. These are behavioral traits not visible in the annotations (which only mark readOnlyHint, openWorldHint, idempotentHint, destructiveHint). The description adds valuable context about waiting, retries, and recovery, going well beyond the structured data.
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 three tightly focused paragraphs: purpose, timing/retry, and prerequisites/recovery. Every sentence earns its place, and the most important information (one lead, synchronous, slow) is front-loaded. There is 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 12-parameter tool with an output schema present, the description covers the essential operational context: how to get a prerequisite campaign_id, how long to wait, what to do on timeout, and how to recover results. Return values are already handled by the output schema, so no description coverage is needed there. The tool is complex, but the description addresses all likely agent uncertainties.
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 covers all 12 parameters with descriptions (100% coverage), so the baseline is 3. The description itself does not add parameter-level semantics beyond what the schema already states. The only relevant note about campaign_id is already in the schema ('Campaign UUID from list_campaigns'), so the description does not compensate further.
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 opens with a specific verb and resource: 'Generate a personalized cold email sequence for ONE lead.' The scope is clearly delimited to a single lead, and the emphasis on 'ONE lead' distinguishes it from the generate_batch sibling. The rest of the description reinforces this purpose by explaining the synchronous nature and recovery flow.
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?
Provides explicit prerequisite guidance: 'You must have a campaign_id first. Call list_campaigns if you don't have one.' It also gives clear recovery instructions on timeout ('use the returned list_id with get_list_status and export_list') and tells the agent not to retry when slow. This is more than enough for an agent to choose and invoke the tool correctly.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Check the processing status of a lead list.
Use this to POLL after calling generate_batch. Call every 15-30 seconds until processing_status is 'completed' or 'failed'. When completed, call export_list. When failed, submit a new batch.
| Name | Required | Description | Default |
|---|---|---|---|
| list_id | Yes | List UUID from generate_batch or generate_sequence response. |
| Name | Required | Description |
|---|---|---|
| result | Yes |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Beyond the annotations (readOnlyHint, idempotentHint), the description discloses polling behavior, the expected check cadence, and the status values that indicate completion or failure. This adds operational context not present in the schema or 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 front-loaded with the core purpose, followed by terse, actionable instructions. Each sentence serves a distinct function with no redundancy, fitting within three lines.
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?
The tool has a single parameter, an output schema, and thorough annotations. The description covers the operational workflow (poll, terminal states, next actions) comprehensively, so nothing essential is missing.
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 provides complete coverage (100%) for list_id, describing it as the UUID from generate_batch or generate_sequence response. The description does not add new parameter semantics but indirectly reinforces the source of the ID, so the 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 'Check the processing status of a lead list,' using a specific verb and resource. It distinguishes this tool from siblings like generate_batch, export_list, list_lists, and list_campaigns by focusing on status polling for a generated list.
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 explicit instructions to use this tool after calling generate_batch, with a polling interval of 15-30 seconds. It also specifies terminal conditions and next actions, making it clear when to use this vs alternatives.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
List campaigns in the user's MachFive workspace.
CALL THIS FIRST before generate_sequence or generate_batch — you need a campaign ID to generate emails. If the user hasn't specified a campaign, call this and ask them to pick one.
Returns JSON array of campaigns with id, name, and created_at. Use the 'id' field as campaign_id in generate calls.
| Name | Required | Description | Default |
|---|---|---|---|
| query | No | Search string to filter campaigns by name (case-insensitive substring match). Leave empty to list all. |
| Name | Required | Description |
|---|---|---|
| result | Yes |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare read-only, idempotent, and non-destructive behavior, and the description adds useful context about the return format (array with id, name, created_at) and how to use the id as campaign_id. No contradictions with 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 three sentences with clear structure: what it does, when to use it, and what it returns. No 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?
The tool is simple, and the description covers purpose, workflow, and output, complemented by a fully documented schema and a true output schema. The description is sufficient for an agent to select and invoke the tool 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 single parameter `query` is fully described in the input schema with a clear description (case-insensitive substring filter), so the description doesn't need to add more. It does reinforce that listing returns all campaigns when no filter is applied.
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 opens with a specific action ('List campaigns') and identifies the resource ('in the user's MachFive workspace'). It also distinguishes its role from the generate tools by explicitly stating it should be called first to obtain a campaign ID.
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 when-to-use guidance: 'CALL THIS FIRST before generate_sequence or generate_batch' and instructs to ask the user to pick a campaign if none is specified. This gives clear direction against alternatives.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
List lead lists (batch jobs) in the user's MachFive workspace.
Useful for browsing past batches, checking what's in progress, or finding a list_id to export. Results are ordered newest first.
| Name | Required | Description | Default |
|---|---|---|---|
| limit | No | Max results to return, 1-100. | |
| offset | No | Pagination offset. | |
| status | No | Filter by processing status: 'pending', 'processing', 'completed', or 'failed'. | |
| campaign_id | No | Filter by campaign UUID. |
| Name | Required | Description |
|---|---|---|
| result | Yes |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint, idempotentHint, and destructiveHint, covering the safety profile. The description adds useful behavioral context beyond annotations by noting results are ordered newest first and explaining typical use cases. This goes beyond simple annotation repetition.
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 concrete use cases. Every sentence earns its place with no wasted words, making it highly concise 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 presence of an output schema (handling return values) and fully documented parameters, the description covers purpose, usage context, and an important behavioral trait (ordering). It is complete for a simple read-only list tool and aligns well with sibling tools.
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 each parameter already well-described in the input schema. The description itself does not add parameter-level detail, so it meets the baseline for high schema coverage without needing to compensate.
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 lists lead lists (batch jobs) in the user's workspace, using specific verb+resource. It distinguishes from sibling tools like list_campaigns (which lists campaigns) and get_list_status (single status) by focusing on the collection of lead lists.
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 clear context on when to use the tool: for browsing past batches, checking progress, or finding a list_id to export. It does not explicitly name alternatives or exclusions, but the use cases imply when it's appropriate, which earns a 4 rather than a 5.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Recent tool additions, removals, and schema changes observed during successful MCP inspections. Dates show when Glama detected each change.
export_list1 field changedOutput schema / descriptionAdded value: +"Generic wrapper for non-object return types."generate_batch1 field changedOutput schema / descriptionAdded value: +"Generic wrapper for non-object return types."generate_sequence1 field changedOutput schema / descriptionAdded value: +"Generic wrapper for non-object return types."get_list_status1 field changedOutput schema / descriptionAdded value: +"Generic wrapper for non-object return types."list_campaigns1 field changedOutput schema / descriptionAdded value: +"Generic wrapper for non-object return types."list_lists1 field changedOutput schema / descriptionAdded value: +"Generic wrapper for non-object return types."export_list2 fields changedInput schema / properties / format / descriptionAdded value: +"Output format: 'json' (structured data) or 'csv' (raw CSV for sending tools)."Input schema / properties / list_id / descriptionAdded value: +"List UUID to export."generate_batch7 fields changedInput schema / properties / approved_ctas / descriptionAdded value: +"Comma-separated CTAs. Omit to use campaign defaults."Input schema / properties / campaign_angle / descriptionAdded value: +"Additional context/angle for personalization."Input schema / properties / campaign_id / descriptionAdded value: +"Campaign UUID from list_campaigns."Input schema / properties / email_count / descriptionAdded value: +"Number of emails per lead, 1-5."Input schema / properties / email_signature / descriptionAdded value: +"Signature appended to each email."Input schema / properties / leads_json / descriptionAdded value: +"JSON array of lead objects. Each MUST have \"email\". Optional: name, title, company, company_website, linkedin_url. Example: '[{\"email\":\"jane@acme.com\",\"name\":\"Jane Doe\"}]'"Input schema / properties / list_name / descriptionAdded value: +"Display name for this batch in MachFive UI."generate_sequence12 fields changedInput schema / properties / approved_ctas / descriptionAdded value: +"Comma-separated CTAs, e.g. 'Direct Meeting CTA, Lead Magnet CTA'. Omit to use campaign defaults."Input schema / properties / campaign_angle / descriptionAdded value: +"Additional context/angle for personalization."Input schema / properties / campaign_id / descriptionAdded value: +"Campaign UUID from list_campaigns."Input schema / properties / company / descriptionAdded value: +"Lead's company name (improves personalization)."Input schema / properties / company_website / descriptionAdded value: +"Company URL for AI research."Input schema / properties / email / descriptionAdded value: +"Lead's email address (REQUIRED)."Input schema / properties / email_count / descriptionAdded value: +"Number of emails in sequence, 1-5."Input schema / properties / email_signature / descriptionAdded value: +"Signature appended to each email."Input schema / properties / linkedin_url / descriptionAdded value: +"LinkedIn profile URL for deeper personalization."Input schema / properties / list_name / descriptionAdded value: +"Display name for this list in MachFive UI."Input schema / properties / name / descriptionAdded value: +"Lead's full name (improves personalization)."Input schema / properties / title / descriptionAdded value: +"Lead's job title (improves personalization)."get_list_status1 field changedInput schema / properties / list_id / descriptionAdded value: +"List UUID from generate_batch or generate_sequence response."list_campaigns1 field changedInput schema / properties / query / descriptionAdded value: +"Search string to filter campaigns by name (case-insensitive substring match). Leave empty to list all."list_lists4 fields changedInput schema / properties / campaign_id / descriptionAdded value: +"Filter by campaign UUID."Input schema / properties / limit / descriptionAdded value: +"Max results to return, 1-100."Input schema / properties / offset / descriptionAdded value: +"Pagination offset."Input schema / properties / status / descriptionAdded value: +"Filter by processing status: 'pending', 'processing', 'completed', or 'failed'."export_listgenerate_batchgenerate_sequenceget_list_statuslist_campaignslist_listsClaiming proves that you control a remote MCP connector. It does not move, proxy, or interrupt the server.
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Each tool has a distinct purpose: list_campaigns provides campaign IDs, generate_batch and generate_sequence handle async batch and sync single generation respectively, get_list_status polls progress, export_list retrieves completed results, and list_lists browses past batches. The descriptions clearly differentiate the overlapping generate tools with explicit usage guidance.
All tool names follow a consistent verb_noun pattern with snake_case (e.g., list_campaigns, generate_batch, export_list). The verbs are clear and the nouns accurately reflect the resources/actions, making the API predictable.
With 6 tools, the server is well-scoped for its purpose of generating and managing email sequences. Each tool serves a necessary role in the workflow, and the count is ideal—not overwhelming, not sparse.
The tool set covers the full lifecycle: discover campaigns, submit batch or single-lead generation, poll status, retrieve results, and browse past batches. There are no obvious missing operations that would prevent an agent from completing the core workflow.
