Finds verified B2B leads and writes an outreach email in English or Arabic.
Scored across 1 tool
Only one tool exists, so there is no risk of selecting the wrong tool or confusing overlapping purposes. Its purpose is clearly stated as preparing a B2B cold-outreach campaign.
The single tool uses a clear snake_case verb_noun convention (prepare_campaign). With only one name there are no inconsistencies, though the pattern cannot be verified across a broader set.
A single tool is thin for a sales-agent server, but the tool bundles several operations (lead discovery, email verification, template writing), making the surface feel more like a workflow endpoint than a complete toolkit. It is defensible for a narrowly scoped public service, but borderline.
The tool covers the preparation step end-to-end, returning leads and an email template. However, there are notable gaps: no way to send the campaign, manage or retrieve previously prepared campaigns, edit the template, or run lead discovery separately from email generation.
Prepare a B2B cold-outreach campaign. Finds up to 5 decision makers who match the target role, keywords and location (LinkedIn and Apollo), keeps only people whose work email Apollo has verified, and writes one outreach email template for them in English or Arabic that states only what the offer says. Returns the leads (name, title, organization, work email, LinkedIn URL) and the email (subject, preheader, greeting, intro, highlights, closing, cta_label) with {{first_name}} style placeholders. Takes about 30 to 90 seconds. Public service by JIR Ventures with a daily limit.
| Name | Required | Description | Default |
|---|---|---|---|
| offer | Yes | What the company sells or offers, in a few sentences. The email states only what is written here | |
| keywords | Yes | Industry or topic keywords that narrow the search, comma separated, for example fintech, SaaS. Can be empty | |
| location | Yes | City or country of the people to reach, for example Dubai or Saudi Arabia. Use worldwide for no filter | |
| max_leads | Yes | How many leads to return, 1 to 5 | |
| target_role | Yes | Job titles to reach, comma separated, for example Head of Sales, Sales Director | |
| booking_link | Yes | Meeting booking link for the call to action, or empty | |
| company_name | Yes | Name of the company that sends the outreach | |
| requested_by | Yes | Name of the AI assistant or app making this request, if known | |
| email_language | Yes | en for English or ar for Arabic | |
| ideal_customer | Yes | Description of the ideal customer, used for tone, or empty |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries the full burden and does well: it discloses the external sources (LinkedIn, Apollo), the verification filter, the content constraint ('states only what the offer says'), latency, and a daily rate limit. What it omits is failure behavior — what happens if fewer than max_leads qualify, if the daily limit is exhausted, or if Apollo has no verified emails.
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 purpose is front-loaded in the first clause and every subsequent sentence adds new information (sources, filter, output shape, latency, limit) with little repetition of the schema. The single long multi-clause sentence is dense, but trimming it would lose real content.
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 10-required-parameter generative tool with no output schema, the description is unusually complete: it enumerates the returned lead fields and every email section, states latency and rate limits, and pins down the two data sources. Nothing an agent needs to decide whether to call it or to interpret the result 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?
Schema description coverage is 100% across all 10 parameters, so the schema already defines each field with examples. The description adds only marginal param-level meaning, mainly the practical ceiling of 5 leads and the {{first_name}} placeholder convention, which is really output information rather than input semantics. Baseline 3 applies.
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?
States a specific verb and resource ('Prepare a B2B cold-outreach campaign') and then spells out the full pipeline: sourcing decision makers from LinkedIn and Apollo by role/keywords/location, filtering to Apollo-verified work emails, and writing one English or Arabic email template. An agent knows exactly what it will get back (lead fields plus email sections) without opening the schema.
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?
There are no sibling tools, so the description's job is to convey when this is the right call and what constraints apply; it does that via 'about 30 to 90 seconds', 'public service by JIR Ventures with a daily limit', and the max-5-leads scope. It gives clear operating context but no explicit exclusions or conditions under which the call would be wrong.
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.
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