gapup-mcp

abm_lookalike_account_finder

Read-onlyIdempotent

As a CMO, discover 50 B2B accounts that closely match your top 10 customers' tech stacks and firmographics. This tool analyzes public web data including robots.txt and OpenGraph metadata to identify lookalike accounts for targeted ABM campaigns. Input your top customer domains and desired firmographic filters to receive a ranked list of potential targets with matching technologies and company attributes.

Input Schema

TableJSON Schema

Name	Required	Description
`async`	No	If true, returns a job_id immediately (<200ms) instead of waiting for the result. Poll the result with job_result(job_id). Use for slow tools to avoid client timeouts.
`tech_stack_keywords`	No	Specific technologies to match in lookalike accounts
`firmographic_filters`	No
`top_customer_domains`	Yes	List of top 10 customer domains to use as seed accounts

Output Schema

TableJSON Schema

Name	Required	Description	Default
`stats`	No
`status`	Yes
`sources`	Yes
`warnings`	Yes
`lookalike_accounts`	Yes
`matched_technologies`	No

TDQS

A4.4/5.0

Behavior4/5

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

Annotations already declare readOnlyHint, openWorldHint, idempotentHint. The description adds context about data sources (robots.txt, OpenGraph metadata) and mentions output ranking. No contradictions, but could elaborate on async behavior and rate limits.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

Three sentences, each adding value: action, method, inputs/outputs. Front-loaded with the core purpose. No wasted words.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness4/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given the output schema exists, description adequately covers usage. It explains the tool's function and data sources, though it could mention the async parameter as an operational detail.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters4/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema coverage is 75%. Description adds meaning by naming the firmographic filters and stating the output count (50 accounts). It complements the schema without repeating it fully.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the tool's purpose as discovering 50 B2B lookalike accounts based on tech stacks and firmographics. It specifies the input (top 10 customer domains) and output (ranked list), distinguishing it from siblings like account_expansion_mapper.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines4/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description explains when to use the tool (for targeted ABM campaigns) and what inputs are needed. However, it does not explicitly mention when not to use it or compare with alternatives among the sibling tools.

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

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TDQS

C2.5/5.0

Disambiguation2/5

With 271 tools, many have overlapping purposes (e.g., multiple competitor intel tools, multiple financial modelers, multiple ESG auditors). Detailed descriptions help slightly, but the sheer volume creates confusion. Agents would struggle to select the right tool among many similar options.

Naming Consistency1/5

Tool names are wildly inconsistent: mix of English and French, snake_case and short phrases, some very generic (process, run, execute equivalents). No discernible naming convention (e.g., abm_architect vs. boundary_control vs. bp_narratif). This makes it hard to predict tool names.

Tool Count1/5

271 tools is far beyond typical well-scoped servers (3-15). This indicates an unfocused, over-bloated tool surface. Even for a general business intelligence server, this number is excessive and violates the principle of each tool earning its place.

Completeness2/5

Despite the large count, coverage feels scattered. Some domains (e.g., content, competitive intel) have many tools, while others (e.g., supply chain, HR) have gaps. The set lacks a coherent scope; it seems like a dump of many separate tool collections rather than a complete, curated surface.

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