enable_connectorRe-show a previously hidden connector's tools for the current user. Use when the user wants to restore a paused connector, such as 'enable Shopify' or 'show TikTok again'. If the connector still needs authentication, get_connector_status will show the connect link.
| Name | Required | Description | Default |
|---|---|---|---|
| connector_id | Yes | Connector ID to enable. Examples: shopify, tiktok, ebay, microsoft, slack, google_ads, quickbooks, klaviyo. |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations declare idempotentHint=true, meaning re-enabling an already-enabled connector is safe. The description adds context about the tool being non-destructive and that it only affects the current user. It also notes the authentication fallback path, which is helpful beyond the 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?
The description is two sentences long, front-loaded with the core action, and every sentence serves a purpose. No wasted words.
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?
Given the tool's simplicity (1 parameter, no output schema, no nested objects), the description is complete enough. It covers what the tool does, when to use it, and a related tool for authentication. It could mention the typical success/error response format, but for a basic enable action this is sufficient.
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?
Schema coverage is 100% with clear examples in the description. The description adds value by listing common connector IDs (shopify, tiktok, etc.) that the agent can use as examples, going beyond what the schema provides.
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 a specific verb ('re-show') and resource ('previously hidden connector's tools') and provides concrete examples like 'enable Shopify' or 'show TikTok again'. It clearly distinguishes the tool from siblings like disable_connector or close_connector.
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 to use this tool when the user wants to restore a paused connector, and provides example phrases. It also mentions that if authentication is needed, get_connector_status should be used instead. However, it does not explicitly state when not to use this tool (e.g., if the connector is already enabled).
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.