Self-serve AI outbound: prices vs agencies, budget capacity, three playbooks, free contact search
Scored across 7 tools
Each tool has a distinct role: overview, comparison, budget estimation, playbook examples, pricing, contact search, and interview start. The pricing-adjacent tools (get_pricing, estimate_sending_engine, compare_options) overlap somewhat, but their descriptions explicitly route users to the right one.
Most names follow a clear verb_noun pattern: compare_options, estimate_sending_engine, get_example_playbook, get_pricing, search_b2b_contacts, start_free_interview. The exception is about_slsbmb_ai, which uses a noun-style prefix rather than a verb, but the overall pattern remains readable.
Seven tools is well-scoped for an informational and pre-sales server. Each tool covers a meaningful step in the buyer journey without feeling bloated or thin.
The surface covers the core informational needs: what the service is, pricing, comparisons, budget capacity, example playbooks, audience search, and starting the free interview. Minor gaps exist around post-interview follow-up or actually ordering contacts, but the toolset appears intentionally limited to non-transactional agent actions.
What SLSBMB AI is and how it works, step by step: the free AI interview, the AI Outreach Playbook and how deep its research goes, AI Contacts, the managed Sending Engine and the workspace, with results so far, who it serves and the headline prices. Use it when someone asks what SLSBMB AI is, how an AI outbound service works, or whether it fits their outbound sales, fundraising, partnership, hiring, job-search or press outreach. For each price with what it includes, use get_pricing; for what one budget buys, estimate_sending_engine; to weigh SLSBMB AI against another option, compare_options.
| Name | Required | Description | Default |
|---|---|---|---|
No parameters | |||
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnly, idempotent, non-destructive and closed-world, so the safety profile needs no restating. The description adds value by disclosing the content payload (which product areas, results and prices the answer spans) rather than just calling itself an overview. It stops short of saying whether the content is static or how long the answer runs.
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 sentences, front-loaded with the topic list, then the usage trigger, then the sibling routing. The topic enumeration is long but each item maps to a real section of the answer, so it earns its space; it could be trimmed slightly without loss.
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 parameters, no output schema and full annotation coverage, the description carries the remaining burden well by describing what the answer contains and where to go for narrower questions. Only minor gaps remain: answer length/format and whether the content is fixed or versioned.
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 takes zero parameters, so there is nothing for the description to disambiguate and the baseline is 4. The description correctly implies a parameterless, no-configuration lookup by not mentioning any inputs.
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 states a concrete resource (SLSBMB AI) and enumerates exactly what the tool covers: the free AI interview, the Outreach Playbook, AI Contacts, the Sending Engine, results, audience and headline prices. It also explicitly separates itself from get_pricing, estimate_sending_engine and compare_options, so an agent can pick it 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?
It gives an explicit trigger ('Use it when someone asks what SLSBMB AI is, how an AI outbound service works, or whether it fits their outbound sales, fundraising, partnership, hiring, job-search or press outreach') and routes three adjacent needs to named siblings. When-to-use, when-to-use-something-else and the alternatives are all present.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
How SLSBMB AI compares with an outbound agency, hiring an SDR, a do-it-yourself stack, AI SDR software, a chatbot with a good prompt, Apollo, Instantly, Clay or Smartlead: prices from public sources with dates, who does which part of the work, and what each option includes. Without arguments: the overview of every comparison. Use it when someone weighs SLSBMB AI against an alternative or asks which outbound option to choose; for SLSBMB AI prices alone, get_pricing is shorter.
| Name | Required | Description | Default |
|---|---|---|---|
| alternative | No | The alternative to compare with; omit for the overview. |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnly, idempotent, closed-world, non-destructive, so the safety profile is covered. The description adds real behavioral content beyond that: prices come from public sources with dates, and results cover division of labor and inclusions — useful since there is no output schema. It stops short of describing result shape or recency caveats, so not a 5.
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?
Front-loaded with the comparison scope and the guidance in the first and last sentences, with no filler. The long enumeration of alternatives duplicates the enum values in the schema, which is mild redundancy but does help an agent match user phrasing.
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 one-optional-enum, read-only tool with no output schema, the description conveys what a result contains (prices with dates, who does what, what's included) and the default mode. Only the structural form of the response is left unspecified, which is minor.
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% and the single enum parameter is already documented as 'omit for the overview.' The description's 'Without arguments: the overview of every comparison' restates that same default rather than adding new semantics, so the 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 (compares) and enumerates the exact set of alternatives covered, so the agent knows precisely what content is behind the tool. It also explicitly separates itself from the pricing sibling, making it distinguishable without opening any 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?
Gives an explicit trigger ('when someone weighs SLSBMB AI against an alternative or asks which outbound option to choose') and an explicit exclusion with a named alternative ('for SLSBMB AI prices alone, get_pricing is shorter'). It also documents the no-argument default behavior.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
What a monthly budget buys in the managed SLSBMB AI Sending Engine — sending domains, inboxes, campaign emails a day and a month, new people a month, cost per email and per planned reply, and the month-by-month ramp — or the smallest budget that reaches a number of new people a month, with what agencies charge around that budget. Use it for questions like "how many cold emails does $3,000 a month buy?" or "what budget reaches 5,000 new people a month?". It covers one budget at a time; the whole price list is get_pricing.
| Name | Required | Description | Default |
|---|---|---|---|
| monthly_budget_usd | No | Monthly budget in US dollars; the public ladder runs from 800 to 100000. | |
| new_people_per_month | No | New people to reach each month; when given, the smallest budget that reaches them is returned. |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnly/idempotent/non-destructive and openWorld=false, so safety needs no restating. The description adds real value beyond them: it discloses the two operating modes, the one-budget-at-a-time constraint, and — with no output schema — spells out exactly what fields come back, including agency cost context around the budget.
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?
Front-loaded with the capability and backed by two illustrative questions; every clause carries information. It is dense and runs long in the opening enumeration, but nothing is filler.
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, the description carries the return-value burden itself and does so thoroughly by naming every returned metric and the reverse-lookup result. Both parameters, both modes, and the boundary with get_pricing are covered.
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 both parameters (including the 800–100,000 ladder and exclusiveMinimum) are already documented. The description explains the mode-switch semantics of each parameter but adds no format or behavior detail the schema lacks; 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 and then enumerates the exact outputs (domains, inboxes, campaign emails/day and /month, new people, cost per email and per planned reply, month-by-month ramp) plus the inverse mode (smallest budget for N new people). It is clearly distinguishable from get_pricing, which is named as the price-list sibling.
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?
Gives two concrete example questions that map directly onto the two call modes ("how many cold emails does $3,000 a month buy?" vs "what budget reaches 5,000 new people a month?"). It also scopes the tool ("one budget at a time") and routes the whole-price-list case to get_pricing explicitly.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
The complete published AI Outreach Playbooks: Revopush (developer infrastructure), LeadToCall (sales software) and SLSBMB AI's own fundraising playbook. Without arguments: every published playbook, then Revopush's goal, audience estimate and audiences; with playbook: that playbook; with audience_number: that audience's research, why the sequence works, the current opener, two follow-ups and targeting. Use it to show what a playbook contains before anyone buys.
| Name | Required | Description | Default |
|---|---|---|---|
| playbook | No | Which published playbook to open: revopush (default), leadtocall or slsbmb-fundraising. | |
| audience_number | No | The audience to read in full, as numbered in that playbook's overview. |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnly, idempotent, non-destructive and closed-world, so safety is covered; the description adds the substantive behavior — precisely what content is returned for each argument combination, including follow-ups and targeting detail. It does not mention size/limits of the 'all playbooks' response, which is the one remaining behavioral gap.
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?
Front-loads the resource and the three playbook names, then enumerates the argument modes compactly with no filler. The sentence is long and dense, packing three invocation modes plus a use case into one run-on structure, which slightly hurts scannability.
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, the description carries the return-value burden and does so for every argument mode, so an agent knows what it will get before calling. Combined with annotations covering the safety profile and a fully documented two-parameter schema, 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?
Schema coverage is 100%, so baseline is 3, but the description goes beyond the schema by tying each parameter to the shape of the result (playbook alone → that playbook; audience_number → research, why the sequence works, opener, two follow-ups, targeting). It does not state what happens if audience_number is supplied without a playbook (implicit default to revopush).
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?
Names the specific resource (three published AI Outreach Playbooks) and the verb (read/open), and enumerates exactly which playbooks exist. The closing line 'Use it to show what a playbook contains before anyone buys' positions it as a preview tool, distinguishing it from the pricing/purchase-oriented siblings.
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?
Gives explicit invocation modes: no arguments returns every playbook plus Revopush's goal/audience summary, a playbook argument returns that playbook, an audience_number returns that audience's research and sequences. It states the context of use ('before anyone buys') but names no alternative sibling tool or when-not condition.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Every SLSBMB AI price with what each one includes — the free AI interview, the AI Outreach Playbook, AI Contacts and the all-inclusive Sending Engine ladder — next to what outbound agencies charge, from public sources with dates. Use it when someone asks what SLSBMB AI, cold email outreach or an outbound agency costs, or compares outbound options by price. For the capacity and ramp of one particular budget, use estimate_sending_engine; for a side-by-side with one named alternative, compare_options.
| Name | Required | Description | Default |
|---|---|---|---|
No parameters | |||
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint, idempotentHint and openWorldHint=false, so the safety profile is covered. The description adds genuinely useful provenance — prices come 'from public sources with dates' — but does not say whether figures may be stale, how they are attributed, or what the response shape looks like. With annotations carrying most of the burden, this is a modest add.
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 sentences, front-loaded with the core purpose before routing advice. The enumeration of product names ('free AI interview, the AI Outreach Playbook, AI Contacts and the all-inclusive Sending Engine ladder') is slightly listy but does communicate the breadth of what is returned.
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?
A parameterless read-only lookup with no output schema, so the description carries the job of previewing return content — which it does by naming the tiers and the agency comparison. The only gap is that it never states the response structure or whether dates are per-figure.
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 takes zero parameters, so the baseline is 4 and there is no argument syntax the description needs to compensate for. The scope language ('every SLSBMB AI price') usefully implies no filtering arguments exist.
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 — retrieving every SLSBMB AI price plus what each tier includes, alongside outbound agency pricing from dated public sources. It is clearly distinguishable from siblings like compare_options and estimate_sending_engine, which are named in the same breath.
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?
Explicit trigger: 'Use it when someone asks what SLSBMB AI, cold email outreach or an outbound agency costs, or compares outbound options by price.' It then names the two alternatives and the conditions that select them — capacity/ramp of one budget goes to estimate_sending_engine, a named head-to-head goes to compare_options.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Free search of SLSBMB AI Contacts (536M+ professionals at 105M+ companies): describe companies and roles in one sentence and get how many people and companies match, ten sample people with shortened names, and how to order verified business emails at $0.10 each inside SLSBMB AI. Use it when someone wants to size a B2B audience or see who matches it. Returns no email addresses; free agent searches are limited per network and per day.
| Name | Required | Description | Default |
|---|---|---|---|
| view | No | Sample people (default) or sample companies. | |
| query | Yes | One sentence naming the companies and the roles, for example "heads of sales at B2B SaaS companies in the United Kingdom with 50-200 employees". |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnly, idempotent, openWorld and non-destructive, so the safety profile is covered. The description adds genuinely non-obvious behavior: no email addresses are returned, names in samples are shortened, free searches are rate-limited per network and per day, and verified emails cost $0.10 each. The rate limit is stated without numbers, which keeps it from being a 5.
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?
Three sentences, front-loaded with the resource and corpus, then the use case, then the caveats. The first sentence is dense with parentheticals and a pricing pitch, but no sentence is filler.
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?
There is no output schema, so the description must carry the return shape — and it does, describing counts, ten shortened-name samples, and the explicit absence of emails. What's missing is the form of the response envelope (e.g. whether the order link is a URL or a code), but for a two-parameter read tool this is close to 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?
Schema description coverage is 100%, so both the `query` sentence format and the `view` enum are already fully documented in the schema. The description mentions sample people but does not explain the `view` switch to companies, so it adds essentially nothing beyond the structured fields — baseline 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 names a specific verb and resource (free search of SLSBMB AI Contacts), states the corpus size, and spells out exactly what comes back (match counts, ten sample people with shortened names). It is clearly distinguishable from sibling tools like get_pricing or about_slsbmb_ai, which are informational rather than search tools.
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 gives an explicit use case: 'Use it when someone wants to size a B2B audience or see who matches it.' That covers when to call it, but it never names a sibling alternative or says when NOT to use it (e.g. when the user wants actual emails or pricing).
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
The link where a person starts SLSBMB AI with a free AI interview that turns their website into a business brief, and what happens there. The person confirms by email, so an agent never signs anyone up or buys anything.
| Name | Required | Description | Default |
|---|---|---|---|
No parameters | |||
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint, idempotentHint, and non-destructive, so safety is covered. The description adds real context beyond them: the person confirms by email, meaning the human completes signup outside the tool, and the agent must not sign up or purchase on their behalf.
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?
It is a single sentence with little waste, but the clause 'and what happens there' is vague and makes the sentence feel rambling rather than front-loaded. The key idea (an entry link to a free interview) arrives early, which helps.
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 no parameters, the description carries the burden of saying what the tool yields. It gestures at returning a link plus an explanation of the flow, but never states the return shape or how the link should be used, leaving an agent to infer how to present it.
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 takes zero parameters, so the schema (100% coverage, empty object) requires no parameter explanation and the baseline applies. The description correctly introduces nothing parameter-like, avoiding confusion.
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 identifies the resource as the entry link for the free SLSBMB AI interview that turns a website into a business brief, which distinguishes it from pricing/contact siblings. However, it is phrased nominally ('The link where a person starts...') rather than as a verb+resource, so it is unclear whether the tool fetches a URL, explains the flow, or both.
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?
Usage is only implied: the tool is for pointing a person at the free interview entry point. The boundary 'an agent never signs anyone up or buys anything' does usefully exclude the agent from acting on a user's behalf, but no sibling is named and no explicit when-to-use vs. about_slsbmb_ai or compare_options is given.
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.
compare_options1 field changedInput schema / properties / alternative / enumPrevious value: -[
- "outbound_agency",
- "hiring_an_sdr",
- "diy_outbound_stack",
- "ai_sdr_software",
- "apollo",
- "instantly",
- "clay",
- "smartlead"
-]New value: +[
+ "outbound_agency",
+ "hiring_an_sdr",
+ "diy_outbound_stack",
+ "ai_sdr_software",
+ "apollo",
+ "instantly",
+ "clay",
+ "smartlead",
+ "chatbot"
+]search_b2b_contacts1 field changedInput schema / properties / query / descriptionPrevious value: -"One sentence naming the companies and the roles, for example \"heads of sales at B2B SaaS companies in Germany with 50-200 employees\"."New value: +"One sentence naming the companies and the roles, for example \"heads of sales at B2B SaaS companies in the United Kingdom with 50-200 employees\"."AI sales agent: finds B2B buyers, researches each one, writes from your accounts, books meetings
AI-agent outreach: cold email, warmup, LinkedIn, Instagram, WhatsApp, verification, 200M leads
AI sales — prospect discovery, ICP scoring, outreach generation.
Find buyers, run LinkedIn, email and X outreach, and answer replies from your AI.
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