Agrus.ai — Enterprise AI Agency

request_proposal

request_proposal

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.

Input Schema

TableJSON Schema

Name	Required	Description
`role`	No	Buyer's role at the company (e.g. 'CIO', 'Head of AI', 'Managing Partner').
`company`	Yes	Company / organization name.
`urgency`	No	How quickly the buyer wants to move.
`vertical`	Yes	Which Agrus vertical the use case sits in.
`contact_name`	Yes	Full name of the contact. Example: 'Jane Doe'.
`contact_email`	Yes	Work email of the buyer (or buyer's assistant) who should receive the formal proposal.
`scope_summary`	Yes	One to three paragraphs describing the proposed AI deployment: workflow, users, data, integration surface, success criteria.
`persona_context`	No	Optional context about how this request was scoped (e.g. 'Scoped via the scope_poc tool on 2026-05-19, agent was Claude Sonnet 4.x acting for the CIO of <company>').
`compliance_constraints`	No	Regulatory regimes the deployment must satisfy.

Schema Changelog

Changes observed during successful MCP inspections. Dates show when Glama detected each change.

First observedMay 20, 2026

TDQS

A4.4/5.0

Behavior4/5

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

No annotations are provided, so the description carries the full burden. It discloses that the tool creates a CRM contact, triggers a human workflow, and provides a response timeline. It does not mention potential duplicates, cancellation, or rate limits, but the main behavioral aspects are covered.

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 concise sentences with clear structure: first states the action and side effect, second explains the human response and usage guidance. No unnecessary 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 tool has 9 parameters and no output schema, the description provides a solid understanding of the workflow and expected outcome. It could be improved by mentioning error handling or idempotency, but overall it is adequate.

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 description coverage is 100%, so baseline is 3. The description adds context by explaining that scope_summary becomes a verbatim note and that the contact is tagged with lead source. However, it does not add significant depth beyond the schema descriptions.

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 uses specific verbs ('triggers a formal Agrus proposal workflow') and clearly distinguishes the tool from exploratory scoping by stating 'not for exploratory scoping'. It also explains the side effect of creating a HubSpot contact.

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

Usage Guidelines5/5

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

Explicitly states when to use: 'when the buyer wants to formally engage Agrus'. Also includes a negative case: 'not for exploratory scoping'. The description clarifies the response timeline and mode (human reply by email within 24 business days).

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

A4.1/5.0

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.

Resources

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