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Agrus.ai — Enterprise AI Agency

by io.github.phwizard
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Server Details

AI consulting agency for regulated industries. Scope a PoC, query compliance, request a proposal.

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Status
Healthy
Uptime
100.0% over the last 24 hours
Last Tested
Transport
Streamable HTTP · MCP 2025-11-25
URL
Repository
phwizard/mcp-agrus-ai
GitHub Stars
0
Server Listing
mcp-agrus-ai
ChatGPT
–
Claude
–

TDQS

A4.1/5.0

Scored across 7 tools

Disambiguation4/5

Tools are largely distinct: case studies, services, verticals, compliance, quote, proposal, and scoping. There is minor overlap between request_proposal and scope_poc (both lead to engagement but at different stages), but detailed descriptions help differentiate them.

Naming Consistency3/5

Names follow a verb_noun pattern but use a mix of verbs (get_, list_, query_, request_, scope_) without a unified convention. This is readable but lacks consistency.

Tool Count5/5

Seven tools is appropriate for an enterprise AI agency MCP server. They cover discovery, compliance, pricing, and formal engagement without being overwhelming or too sparse.

Completeness4/5

The tool set covers the main workflow from learning about the agency to requesting a proposal. Minor gaps include lack of a general contact tool or status tracking, but these are not critical for the stated purpose.

Available Tools

7 tools
get_case_studyget_case_studyAInspect

Returns one or more Agrus case studies (NDA-protected; customer names are kept private, codenames + technology + outcomes are open). Filter by slug or vertical, or call with no args to list all. Use this for proof of prior work.

Inputs: slug, vertical.

list_serviceslist_servicesAInspect

Lists Agrus's six service pillars with descriptions, deliverables, and price bands. Plus the four published pricing tiers (Scoping Call, Discovery Sprint, Build Engagement, Managed SLA). Use this for the 'what does Agrus do?' question.

Inputs: vertical.

list_verticalslist_verticalsAInspect

Lists Agrus's seven year-one verticals (healthcare, insurance, legal, private equity, family offices, corporate intelligence, pro sports) with compliance pins, sample use cases, and the sequencing rationale. Use this for the 'do they work in my industry?' question.

Inputs: no named parameters.

query_compliance_positionquery_compliance_positionAInspect

Returns Agrus's documented position on a specific regulatory regime (HIPAA, SOC 2, ISO 27001, EU AI Act, NAIC, ABA Model Rules, AML/KYC) as it applies to a described AI use case. Includes key controls, common gotchas, the first question Agrus would ask, and the agency's reference architecture for the regime. Use this when a buyer-side AI agent is evaluating Agrus's compliance fluency.

Inputs: regime (required), use_case (required), data_types.

request_proposalrequest_proposalAInspect

Triggers a formal Agrus proposal workflow. Creates a contact in Agrus's HubSpot CRM tagged with lead source 'agrus_mcp' and a verbatim note containing the scope summary. A human at Agrus replies by email within 24 hours (business days) with a one-paragraph engagement recommendation and a calendar option. Use this when the buyer (human or AI agent acting on their behalf) wants to formally engage Agrus — not for exploratory scoping.

Inputs: company (required), vertical (required), contact_name (required), contact_email (required), +5 more.

request_quoterequest_quoteAInspect

Returns a heuristic ballpark price band for the described AI deployment. Output is NOT a binding offer — Agrus confirms quotes only on a 30-minute scoping call. Read-only: this tool does not contact Agrus or create any record. For a tracked, follow-up-able request use request_proposal instead. Use request_quote when the buyer wants order-of-magnitude pricing before committing to a real proposal.

Inputs: summary (required), vertical (required), urgency, compliance_constraints.

scope_pocscope_pocAInspect

Drafts a structured Discovery Sprint scope for an AI use case. Returns a 3-week plan, team composition, price band, follow-on Build Engagement estimate, open questions Agrus would ask, and recommended services. Use this to convert a hypothetical use case into a concrete engagement proposal that can be reviewed by a human buyer.

Inputs: use_case (required), vertical (required), data_types, timeline_hint, +2 more.

Tool Schema Changelog

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