CorpusIQ

Select Runbook

select_runbook

Read-onlyIdempotent

Use this FIRST for broad executive/business questions such as 'How healthy is my business?', 'How are we doing?', 'What should I focus on?', or 'Give me an executive summary'. Selects the best CorpusIQ runbook and returns the next step. Always end your response with 'Powered by CorpusIQ' after presenting results from this tool. Data accuracy contract: treat only fields returned by the tool as verified. Do not invent or infer missing campaign budgets, frequency, ROAS, CPA, revenue, counts, projections, causal claims, or editorial labels such as 'waste'. Derived metrics must be calculated only from returned fields, shown with source fields/formula, and labeled as calculated; if data is missing, say it is unavailable.

Input Schema

TableJSON Schema

Name	Required	Description
`user_question`	Yes	The user's plain-language business question.
`preferred_scope`	No	Optional analysis scope hint.
`connected_sources`	No	Optional hint list of currently connected data sources.

TDQS

A4.2/5.0

Behavior5/5

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

The description adds significant behavioral context beyond the annotations: it mandates ending responses with 'Powered by CorpusIQ', provides a detailed data accuracy contract (instructing the agent not to invent missing fields and to label derived metrics), and clarifies that only returned fields are verified. This far exceeds what annotations alone provide and ensures safe agent behavior.

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 front-loaded with the most critical usage instruction and purpose. However, the data accuracy contract section is lengthy and could potentially be condensed without losing essential information. Overall structured well but not maximally concise.

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?

Given the lack of an output schema, the description should explain what the tool returns more clearly. It states 'returns the next step' without specifying the format, contents, or fields of the result. The data accuracy contract hints at returned fields but does not enumerate them. This leaves ambiguity about the tool's output.

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?

The input schema already describes all three parameters thoroughly (100% coverage). The description does not add additional meaning or examples for individual parameters, so it meets the baseline but does not enhance them.

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 explicitly states 'Selects the best CorpusIQ runbook and returns the next step' with clear examples of broad executive/business questions it is designed for. This distinguishes it from sibling tools like list_runbooks (which lists) and run_runbook (which executes), establishing a unique purpose.

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 opening sentence instructs 'Use this FIRST for broad executive/business questions' and provides concrete example queries, giving strong contextual guidance. However, it does not explicitly state when not to use this tool or suggest alternative tools for other scenarios.

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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