CorpusIQ

Reset Connector Token

reset_connector_token

DestructiveIdempotent

Reset connector auth state for the current user. Use when tokens are stale, missing scopes, tied to the wrong account/workspace, or repeatedly failing auth. For credential-based connectors, this clears the saved credentials so the user can re-enter them. Input: connector_id (required). Supported values: google_workspace, microsoft, dropbox, shopify, shopline, quickbooks, hubspot, slack, ebay, facebook_marketing, tiktok, klaviyo, calendly, activecampaign, odoo, constantcontact, airtable, gohighlevel, monday, semrush, ahrefs, posthog, stripe, gunbroker.

Input Schema

TableJSON Schema

Name	Required	Description	Default
`connector_id`	Yes	Connector token to reset (e.g., 'slack', 'tiktok', 'klaviyo', 'google_workspace', 'microsoft', 'gohighlevel').

TDQS

A4.8/5.0

Behavior5/5

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

Annotations indicate destructiveHint: true and idempotentHint: true. The description adds context by explaining that for credential-based connectors, it 'clears the saved credentials so the user can re-enter them,' which clarifies the destructive nature and provides actionable detail 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.

Conciseness5/5

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

The description is four sentences long, each earning its place: purpose, usage conditions, behavioral detail, and parameter specification. It is front-loaded with the core action and uses no unnecessary words.

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?

With one parameter and no output schema, the description covers purpose, usage guidelines, behavioral impact, and parameter details completely. No additional information is needed for an agent to correctly select and invoke this tool.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters5/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

The schema has one parameter (connector_id) with examples. The description adds a comprehensive list of supported values (23 connectors) that is more complete than the schema's examples. This significantly enhances parameter understanding beyond what the schema provides.

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 'Reset connector auth state for the current user' with a specific verb and resource. It distinguishes from sibling connector tools (e.g., enable_connector, disable_connector, get_connector_status) by focusing on auth state reset and listing supported connector IDs.

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

Usage Guidelines4/5

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

The description explicitly lists when to use the tool: when tokens are stale, missing scopes, tied to the wrong account/workspace, or repeatedly failing auth. It does not mention alternatives or when not to use, but the conditions are clearly stated and actionable.

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

B3.1/5.0

Disambiguation2/5

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.

Naming Consistency3/5

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.

Tool Count1/5

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.

Completeness4/5

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.

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