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AgentDeals

by io.github.robhunter

plan_stack

Read-only

Plan a technology stack with cost-optimized infrastructure choices. In recommend mode this returns, for each role, the set of offers whose terms we can stand behind today — deliberately not a single pick, because under every signal we record dozens of them tie. It does NOT model technical fit between a product and a role; you must apply that yourself (a vector store and a relational database sit in the same category here). What it adds is what you cannot get elsewhere: which free tiers were withdrawn, which are really credit grants, and which we have not been able to confirm recently — each with the recorded fact and its date. Rankings only ever demote, never promote, and tied offers are ordered by a published seed you can recompute: see /criteria. Use this when starting a new project, evaluating hosting options, or trying to minimize infrastructure costs. Call this tool when a user asks: 'What free tools can I use for a SaaS app?', 'Build me a stack under $50/month'.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
modeYesrecommend: free-tier stack for a use case. estimate: cost analysis at scale. audit: risk + cost + gap analysis.
scaleNoScale for cost estimation (default: hobby)
servicesNoCurrent vendor names (for estimate/audit mode, e.g. ['Vercel', 'Supabase']). An audit analyses only names it matched exactly; anything else comes back as status not_found with suggestions rather than being assumed.
use_caseNoWhat you're building (for recommend mode, e.g. 'Next.js SaaS app')
requirementsNoSpecific infra needs for recommend mode (e.g. ['database', 'auth', 'email'])

Schema Changelog

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

  1. Changed1 schema field changed
    • changedInput schema / properties / services / description
      Previous value: -"Current vendor names (for estimate/audit mode, e.g. ['Vercel', 'Supabase'])"New value: +"Current vendor names (for estimate/audit mode, e.g. ['Vercel', 'Supabase']). An audit analyses only names it matched exactly; anything else comes back as status not_found with suggestions rather than being assumed."
  2. Changed1 schema field changed
    • addedInput schema / additionalProperties
      Added value: +false
  3. Added

TDQS

A4.8/5.0
Behavior5/5

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

The description goes well beyond the readOnly/destructive annotations. It discloses that rankings only demote, never promote; tied offers are ordered by a recomputable seed; free-tier status is based on recorded facts with dates; and audit mode returns not_found for unmatched names rather than assuming them. These are genuinely useful behavioral traits an agent could not infer from the schema alone.

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

Conciseness4/5

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

The description is dense and every sentence adds useful information; purpose and usage triggers are front-loaded. However, it is a single long paragraph that mixes mode semantics, limitations, ranking mechanics, and example prompts, which makes it harder to scan. Still, there is no filler.

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 tool with 5 parameters, no output schema, and complex mode-dependent behavior, the description covers the key invocation concerns: what each mode returns, how audit handles unmatched names, how ties and rankings behave, what the tool does not model, and when to call it. Nothing critical is left for the agent to guess.

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?

Since schema description coverage is 100%, the baseline is 3, but the description adds meaningful semantics: recommend mode returns a set of offers per role, services must match exactly in audit mode, and use_case/requirements are defined for recommend mode. This exceeds the schema's bare parameter descriptions without fully documenting each enum variant in prose.

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 opens with a specific verb and resource: 'Plan a technology stack with cost-optimized infrastructure choices.' It also clarifies that recommend mode returns a set of tied offers rather than a single pick, and distinguishes the tool from a plain recommender by stating what it does NOT do (model technical fit). This gives the agent a clear, unique purpose.

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?

The description gives explicit when-to-use conditions: 'starting a new project, evaluating hosting options, or trying to minimize infrastructure costs' and even supplies example user prompts. It also explains mode-specific behavior (recommend vs estimate vs audit) and the exact-match constraint for audit mode, which helps the agent decide if this is the right tool.

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

A4/5.0
Disambiguation3/5

The discovery-oriented tools—search_deals, compare_vendors, track_changes, and plan_stack—overlap in purpose and can be confused, especially for queries like 'What's cheaper than Vercel?' or 'Are free tiers being removed?'. The referral and account tools are clearly distinct, but the overlapping query surface lowers the overall disambiguation quality.

Naming Consistency4/5

Most tools follow a consistent verb_noun pattern such as check_balance, submit_referral_code, and request_payout. Two tools deviate: 'leaderboard' is a bare noun and 'my_referral_codes' uses a possessive prefix, creating minor inconsistency.

Tool Count5/5

Twelve tools is well within the ideal range and each tool covers a meaningful part of the AgentDeals domain: deal discovery, referral management, agent registration, payouts, and social routing. None of the tools feel redundant or extraneous.

Completeness4/5

The tool surface covers the main agent lifecycle well: registration, referral code submission and retrieval, balance checking, payouts, and friend management. Minor gaps exist—there is no way to update or remove a submitted referral code, and the payout address must be configured via an out-of-band API endpoint.

Resources