GTM data layer for AI agents: find scored B2B leads free, reveal verified email+mobile, audit ads.
Scored across 3 tools
Each tool has a clearly distinct purpose: auditing ads, finding leads, and revealing contact details. No overlap or ambiguity.
All tool names follow a consistent verb_noun snake_case pattern: audit_ads, find_leads, reveal_contact.
Three tools is appropriate for the server's focused purpose of ad auditing and lead generation. Each tool serves a necessary function without redundancy.
The tools cover the core workflow of auditing ads and finding/revealing leads. Minor gaps exist, such as no tool for managing or updating leads after revelation, but the set is well-scoped for its intended use.
Audit a connected Meta ad account (via Adspirer): scores each ad, flags wasted spend, and returns prioritized actions. Spends 5 credits. Omit accountId to audit the demo account.
| Name | Required | Description | Default |
|---|---|---|---|
| days | No | Lookback window in days (default 30). | |
| accountId | No | Adspirer ad_account_id (optional → demo). | |
| roasTarget | No | Target ROAS (default 2.5). |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Description states 'Spends 5 credits', disclosing a cost behavior. Also describes the audit process (scores ads, flags waste, returns actions). With no annotations, this provides reasonable transparency about what happens.
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 information-dense sentences with no wasted words. Action, product, cost, and special case are all front-loaded.
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?
Description is adequate for a simple tool with 3 optional parameters, but lacks output details (no schema) and could mention related tools or side effects. Credit cost is useful but otherwise minimal context.
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?
Input schema has 100% description coverage, so baseline is 3. Description adds marginal value by noting default values and the demo account behavior, but these are already implied in the schema 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?
Description clearly states the tool audits a Meta ad account, with specific actions: scores ads, flags wasted spend, returns prioritized actions. Verb 'audit' + resource 'connected Meta ad account' is distinct from siblings (find_leads, reveal_contact).
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?
Description mentions omitting accountId to audit demo account, implying optional usage, but lacks explicit when-to-use or when-not-to-use guidance. No mention of alternatives or prerequisites.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Find scored, in-market B2B leads (hot-signal) for an offer + ICP. Returns a list with a fit score and a call brief, but WITHOUT contacts — call reveal_contact with a row's ref to get email/phone. Searching is FREE: credits are only spent on reveals.
| Name | Required | Description | Default |
|---|---|---|---|
| icp | Yes | Who you sell to (industry, size, geo, role). | |
| count | No | How many leads (1-25, default 10). | |
| offer | Yes | What you sell, in a sentence. |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations provided, so description bears full burden. It discloses the output (scored leads, fit score, call brief, no contacts) and the pricing model (free search, credits on reveal). This is transparent for a search tool, though potential data freshness is not mentioned.
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 with no wasted words. It front-loads the primary action and efficiently conveys key usage notes.
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 no output schema, the description adequately explains what the tool returns (scored leads, fit score, call brief, no contacts) and the pricing model. This is sufficient for an agent to understand the tool's behavior and integration with 'reveal_contact'.
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?
Input schema has 100% description coverage for its 3 parameters. The description adds minimal extra meaning beyond schema descriptions; it references the output 'ref' but does not elaborate on parameter syntax or constraints. 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?
The description clearly states it finds scored B2B leads for a given offer and ICP, using a specific verb-resource combination. It distinguishes from sibling tools like 'reveal_contact' which handles contact details.
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 that this tool returns leads without contacts and directs the agent to use 'reveal_contact' with a row's 'ref' for contact info. It also clarifies that searching is free and credits are only spent on reveals, guiding appropriate usage.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Reveal a verified email + mobile for a lead ref returned by find_leads. Spends 12 credits (email 2 + mobile 10); fully refunded if nothing verifiable is found — you are never charged on a miss.
| Name | Required | Description | Default |
|---|---|---|---|
| ref | Yes | The `ref` from a find_leads row. |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries full burden. It discloses the credit cost (12 credits, broken down) and the refund policy if nothing verifiable is found. It does not mention authorization requirements or side effects, but the credit transparency is strong.
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: first states purpose, second adds credit/refund info. It is compact, front-loaded, and every sentence adds necessary information with 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?
For a simple single-parameter, no-output-schema tool, the description covers purpose, prerequisite (ref from find_leads), credit cost, and refund policy. It is complete enough for an agent to use correctly without needing additional clarification.
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% for the single 'ref' parameter, and the schema description already states it comes from find_leads. The description does not add new parameter details beyond what the schema provides, so 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 the verb 'reveal' and the specific resources ('verified email + mobile') for a lead ref from find_leads. It distinguishes itself from sibling find_leads by specifying the input source and output.
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 ties usage to having a ref from find_leads, and mentions credit spending and refund policy. However, it does not explicitly state when not to use this tool or provide direct comparison with sibling audit_ads, though the different purpose makes it clear.
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.
audit_adsfind_leadsreveal_contactB2B lead generation, email verification, company enrichment, and agentic GTM Ops.
Search companies, enrich contacts, and reveal emails and phones from your AI agent.
Real-time B2B data for agents: search and enrich 1B+ people and 200M+ company profiles.
Agentic AI for business intelligence: discover, verify and enrich company and contact data.
