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mcp-knowledge

by io.github.getgapup

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
NameRequiredDescriptionDefault
asyncNoIf 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_keywordsNoSpecific technologies to match in lookalike accounts
firmographic_filtersNo
top_customer_domainsYesList of top 10 customer domains to use as seed accounts

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
statsNo
statusYes
sourcesYes
warningsYes
lookalike_accountsYes
matched_technologiesNo

TDQS

A4/5.0
Behavior4/5

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

Annotations already declare readOnlyHint, openWorldHint, and idempotentHint. The description adds valuable behavioral context by specifying the data sources (robots.txt, OpenGraph metadata) and output (ranked list with matching technologies and attributes), going beyond annotation information.

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?

Two sentences front-loaded with purpose, no wasted words. Efficiently conveys what the tool does, inputs, and output.

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 presence of an output schema, the description adequately explains the tool's function and inputs. It could mention async behavior or result polling, but that may be covered by other tools. Overall, it is fairly complete.

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

Parameters3/5

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

Schema coverage is high (75%), and the description provides high-level context (e.g., 'targeted ABM campaigns') but does not add significant detail beyond what the schema already describes for each parameter. Baseline 3 is appropriate.

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: discovering 50 lookalike B2B accounts based on top 10 customer domains and firmographics. It uses specific verbs ('discover') and resources ('lookalike accounts'), and the unique focus on ABM differentiates it from siblings.

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

Usage Guidelines3/5

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

The description implies usage by describing inputs (customer domains, firmographic filters) but does not explicitly state when to use this tool instead of alternatives or when not to use it. No exclusion criteria are provided.

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

C2.8/5.0
Disambiguation2/5

Many tools have overlapping purposes, especially in competitive intelligence, ESG, and risk assessment. For example, there are multiple tools for competitor analysis (competitive_deep_dive, competitor_intel, competitor_moves, etc.) with unclear boundaries. Agents would struggle to select the correct tool without deep understanding of subtle differences.

Naming Consistency2/5

Tool names are a mix of English and French, and follow no consistent pattern. Some use snake_case (e.g., abm_architect, action_plan_esg), while others are verb-focused (e.g., content_catalog, fx_rate). The lack of a uniform naming convention makes it hard for agents to predict tool names.

Tool Count1/5

With 271 tools, the server is excessively large. Even for a broad knowledge domain, this number of tools makes discovery and selection inefficient. Typical coherent servers have 3-15 tools; this has an order of magnitude more, indicating poor scoping.

Completeness3/5

The tool set covers many domains (compliance, finance, marketing, HR, etc.), but the coverage is uneven due to redundancy. Key areas have multiple overlapping tools, while some sub-domains may still have gaps. Overall, the surface is broad but not well-curated.

Resources