Marketing Intelligence & Optimization

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

Annotations already declare readOnlyHint=true and destructiveHint=false, and the description adds key behavioral context: 'MMM is directional (not causal); under-powered A/B tests never declare a winner' and 'Layers run only when their inputs are supplied.' It also discloses the HTTP endpoint and primary output shape, enriching the agent's understanding without contradicting 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 long but well-structured: it opens with a strong imperative, then details capabilities, constraints, and lists of when/when-not. There is some redundancy, such as repeatedly saying 'ALWAYS call... never compute yourself,' but given the tool's complexity the length is justified.

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?

The description covers all 6 analytical domains, row-size limits, exclusions, endpoint, output, and a clear sibling alternative. Combined with the output schema and annotations, an agent has everything needed to invoke this tool correctly for any supported marketing question.

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 description coverage is 100%, so the schema itself thoroughly documents each parameter's meaning. The description adds layer-activation semantics ('Layers run only when their inputs are supplied') and the critical row-count constraint, which helps the agent decide which parameters to populate. This exceeds the baseline 3.

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 states a specific verb+resource: 'market basket / association rules, Marketing Mix Modelling + budget reallocation, A/B significance + optional mSPRT, Bass diffusion, and unit economics CAC/LTV — in one request.' It also distinguishes from the sibling by explicitly directing larger payloads to marketing_intelligence_get_engine.

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?

It has dedicated 'When to call this tool' and 'When NOT to call this tool' sections, including clear alternatives (e.g., 'use marketing_intelligence_get_engine for any array larger than 200 rows'). It also lists non-tasks like unrelated marketing tasks and mental-math calculations, providing thorough usage guidance.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

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

Annotations readOnlyHint=true, openWorldHint=true, destructiveHint=false are consistent with the description. The description adds extensive behavioral context beyond annotations: it returns a script to run in a sandbox, rows never pass through as tokens, script writes to tmp/mi_run/, files must stay under tmp/mi_run/, path lines may read user's files, script must be saved verbatim, and only PATH/option lines are editable. No contradiction.

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 long and contains redundant information (e.g., 'For ≤200 rows use marketing_intelligence instead' appears in both the opening paragraph and the not-call list). It is well-structured with sections, but not concise; every sentence does not earn 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 highly complex tool with 13 parameters and no required parameters, the description is extremely complete: it covers data size thresholds, environment requirements, output location, safety constraints, and alternative usage. Output schema exists, so return values needn't be detailed, but the high-level workflow is fully described.

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 description coverage is 100%, so the baseline is 3. The description does not add per-parameter semantics beyond the schema, but it provides helpful context that all paths refer to user's local files that the script reads in place and that only PATH/option lines may be edited. This is marginal added meaning, so a 3 is appropriate.

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 clearly states a specific verb+resource: 'Get Analysis Script' that returns a complete, runnable Python script for large marketing data analytics. It distinguishes itself from the sibling tool by explicitly saying 'For ≤200 rows use marketing_intelligence instead,' making the purpose unambiguous.

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 includes explicit 'When to call' and 'When NOT to call' sections, naming the alternative (marketing_intelligence for ≤200 rows), conditions (code sandbox required), and exclusions (non-marketing forecasts, rewriting script). It also opens with 'ALWAYS call this tool... for payloads LARGER than 200 rows,' providing clear when-to-use guidance.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.