l0-py-blocks-mcp

add_sso_credential

Add social login credentials for OAuth providers (Google, Facebook, GitHub, etc.).

Args: provider: OAuth provider name (e.g., "google", "facebook", "github") client_id: OAuth client ID from provider console client_secret: OAuth client secret from provider console project_key: Project key (tenant ID). Uses global tenant_id if not provided is_enable: Whether to enable this SSO provider (default: True) redirect_uri: OAuth redirect URI (optional)

Returns: JSON string with SSO credential save result

Input Schema

TableJSON Schema

Name	Required	Default
`audience`	No	http://localhost:3000
`provider`	Yes
`client_id`	Yes
`is_enable`	No
`project_key`	No
`redirect_uri`	No	http://localhost:3000/login
`client_secret`	Yes

Output Schema

TableJSON Schema

Name	Required	Description	Default
`result`	Yes

TDQS

A3.6/5.0

Behavior2/5

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

With no annotations, the description carries the full burden of behavioral disclosure. It indicates a mutation (add/save credentials) and notes the project_key fallback and is_enable default, but omits permissions, idempotency, overwrite behavior, or side effects. That's a significant gap for a write operation.

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 well-structured: a one-sentence purpose, a clear Args list, and a Returns line. It is concise without being terse, though the parameter list takes up several lines. Each line adds value.

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

Completeness3/5

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

The tool has 7 parameters, no annotations, and moderate complexity. The description covers most parameters and the return type but lacks usage guidance and omits the audience parameter. It also doesn't address relationships to sibling tools or potential side effects, making it partially complete.

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?

Schema description coverage is 0%, so the description must compensate. It explains 6 of 7 parameters, including defaults, examples for provider, and the project_key fallback. However, it omits the 'audience' parameter entirely, which is notable since it has a default in 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?

The description clearly states the action: 'Add social login credentials for OAuth providers' followed by concrete examples (Google, Facebook, GitHub). This is a specific verb+resource combination that distinguishes it from sibling tools like activate_social_login.

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

Usage Guidelines3/5

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

The description implies the tool is used to add SSO credentials but provides no explicit when-to-use vs alternatives or exclusions. It doesn't mention how this relates to activate_social_login or other auth-related siblings, so usage context is only implicit.

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

A3.5/5.0

Disambiguation4/5

The tools are organized into clear domains (auth, roles, schemas, translations, projects), and within each domain, tools have distinct purposes (e.g., get_schema vs list_schemas, save_captcha_config vs update_captcha_status). A few pairs like get_auth_status and get_authentication_config could cause minor confusion, but their descriptions clarify the difference.

Naming Consistency5/5

All tool names follow a consistent verb_noun pattern in snake_case, using predictable verbs like get, list, create, update, set, enable, and configure. Even longer names like save_module_keys_with_translations adhere to the same convention, with no mixed casing or arbitrary styles.

Tool Count2/5

With 36 tools, the server exceeds the typical well-scoped range for MCP servers. While the broad platform domain justifies some breadth, the sheer volume can overwhelm agents and suggests the toolset could be consolidated or split into smaller, focused servers.

Completeness3/5

The toolset provides decent coverage for creation, listing, and some updates across entities like permissions, roles, schemas, and translations. However, there are notable gaps: no delete operations for most resources, no role update, and no project update/delete, which leaves lifecycle management incomplete.

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