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Agrus.ai — Enterprise AI Agency

request_quote

request_quote

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.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
summaryYesTwo or three sentences describing the AI deployment the buyer wants a ballpark quote for. Include workflow, users, and integration surface where possible.
urgencyNoFree-form timeline indicator.
verticalYesWhich Agrus vertical the use case sits in.
compliance_constraintsNoRegulatory regimes the deployment must satisfy. Drives the compliance overlay on the quote.

Schema Changelog

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

  1. First observed

TDQS

A4.7/5.0
Behavior5/5

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

Discloses that the tool is read-only, does not contact Agrus or create records, and that output is not binding. Given no annotations, description fully covers behavioral traits.

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 with no waste: first states purpose, second clarifies behavior, third gives usage guidance and alternative. Front-loaded and efficient.

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

Completeness5/5

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

For a read-only heuristic price tool with no output schema, description explains output nature, behavior, and when to use. No missing context given tool simplicity.

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?

All 4 parameters have schema descriptions (100% coverage), so description adds minimal value beyond field names. The description does not elaborate on parameter usage beyond what schema provides.

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?

Clearly states 'Returns a heuristic ballpark price band for the described AI deployment', specifying verb, resource, and scope. Distinguishes from sibling 'request_proposal' by contrasting non-binding estimate vs tracked request.

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 order-of-magnitude pricing before committing to a real proposal' and when not: 'For a tracked, follow-up-able request use request_proposal instead.' Provides clear alternative.

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.

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