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sebbi.pro - the proof layer for AI

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sebbi_create_account

Opens a sebbi.pro account and returns the API key. Only call after showing sebbi_terms and the person agreeing; pass the terms_version they agreed to and customer_agreed=true. The agreement is sealed into the chain. Free for 90 days.

Input Schema

NameRequiredDescriptionDefault
nameNo
emailYes
companyNo
devicesNoEstimated devices; billing uses the real count
productNo
terms_versionYesterms_version from sebbi_terms
customer_agreedYestrue only if the customer read the terms and said yes

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observed

TDQS

A4.4/5.0
Behavior4/5

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

Annotations already declare readOnly=false, idempotent=false, and openWorld=true, so safety profile is covered. The description adds real context beyond that: it returns a credential (API key), the agreement is sealed permanently into the chain, and it is free for 90 days. These are useful behavioral traits, though durability/irreversibility of account creation itself is only implied.

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?

Four tight sentences, front-loaded with verb, resource, and return value, then prerequisites, then side effect, then pricing. Every sentence carries distinct information with no waste.

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?

For a 7-parameter mutation tool with no output schema, the description covers the return value, the consent gating, the chain-sealing side effect, and trial pricing. It leaves the product enum and non-required fields unexplained, but annotations carry the safety profile, so it is nearly 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 description coverage is low (43%), so the description ideally compensates, but it only elaborates on terms_version and customer_agreed—both already documented in the schema. The 'product' enum options and unannotated fields (name, email, company) get no added meaning, so it adds marginal value over the schema.

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?

States a specific verb and resource ('Opens a sebbi.pro account'), names the return value ('returns the API key'), and references the sibling 'sebbi_terms' it depends on. An agent can distinguish this creation tool from verification/check siblings without opening any schema.

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?

Explicit precondition ('Only call after showing sebbi_terms and the person agreeing') plus the exact argument values to pass ('terms_version they agreed to and customer_agreed=true'). Routes the agent to the predecessor tool and specifies the gating condition, leaving nothing to inference.

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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