logout_allLog out of ALL connected data sources at once. Deletes all stored OAuth tokens for the current user and returns re-authentication links for each disconnected connector. Use this to fully reset your session or switch accounts across all services.
| Name | Required | Description | Default |
|---|---|---|---|
No arguments | |||
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already indicate destructiveHint=true (mutation) and idempotentHint=true (safe to repeat). The description adds valuable behavioral context beyond annotations: it explains the exact destructive action (deleting OAuth tokens), the return value (re-authentication links), and the scope (all connectors). There is no contradiction with annotations.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
Two sentences with no extraneous information. First sentence states the action and outcome; second sentence adds return value and usage scenario. Every word earns its place.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a zero-parameter tool with destructive annotations and no output schema, the description covers all essential information: what it does, what side effects occur, what the user gets back, and when to use it. Nothing is missing.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The tool has zero parameters and 100% schema coverage, so there is little for a description to add. The description does not discuss parameters because none exist, which is appropriate. Baseline is 4 for a no-parameter tool.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description uses specific verbs ('Log out', 'Deletes', 'returns') and clearly identifies the resource ('ALL connected data sources', 'stored OAuth tokens', 're-authentication links'). It distinguishes this tool from siblings like 'reset_connector_token' or 'disable_connector' by emphasizing bulk logout across all services at once.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description explicitly states when to use the tool: 'to fully reset your session or switch accounts across all services.' This gives clear context. Although it does not mention specific alternatives among siblings, the scope ('ALL connected data sources') differentiates it from single-connector tools like 'reset_connector_token' or 'disable_connector'.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Add one secure layer between your agents and this server.
Several tools have overlapping purposes: query_database also covers MSSQL alongside query_mssql_database, and list_database_tables overlaps list_mssql_tables. get_user_statistics duplicates get_my_usage_stats, and runbook/skill selection tools (select_runbook, invoke_skill, run_runbook) have fuzzy boundaries. Most connectors are clearly named by source, but these redundancies create real misselection risk.
The dominant pattern is `<source>_connector` for the many integrations, which is consistent. However, the rest mixes styles: `get_*`, `list_*`, `query_*`, `search_*`, and domain-specific families like `canonical_facts_*` vs `canonical_context_get` vs `canonical_decisions_add`. The naming is readable but not uniform.
123 tools is far beyond any reasonable scope for a single MCP server. Even for a multi-service data platform, the catalog is bloated and will overwhelm an agent's context and tool-selection accuracy.
The server covers a wide range of data sources (CRM, ads, email, SEO, ecommerce, finance, databases, YouTube) plus meta-capabilities like canonical facts, metric specs, truth sources, and runbooks. Minor gaps exist (e.g., most connectors are read-only, and some umbrella tools may not expose every operation), but the core intent of querying and analyzing business data is well served.