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mcp-knowledge

by io.github.getgapup

content_audience_profile

Read-only

Return the audience targeting profile of a content entity — its enrichment tags reframed as audience facets with confidence, corroboration and full provenance (verifiable, sourced). The response also carries an entity-level provenance block (average confidence, data freshness). When to use this tool: an ad-tech or marketing agent needs a machine-readable, verifiable audience descriptor for a franchise or work. Input: an entity_id and its type.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
asyncNoIf true, returns a job_id immediately (<200ms) instead of waiting for the result. Poll the result with job_result(job_id). Use for slow tools to avoid client timeouts.
entity_idYesEntity id from content_catalog
entity_typeNo

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
entity_idYes
provenanceYesEntity-level trust & freshness summary.
entity_typeYes
audience_facetsYesMap facet → array of { label, confidence, corroboration, source_count, sources }

TDQS

A4.3/5.0
Behavior4/5

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

Annotations already provide readOnlyHint and openWorldHint. The description adds behavioral context: 'The response also carries an entity-level provenance block (average confidence, data freshness)'. No contradictions.

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?

Concise, front-loaded with purpose, then usage, then input requirements. No redundancy.

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 output schema present and annotations covering safety, the description provides sufficient context for a read-only tool. It explains the output structure and provenance block.

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?

Schema coverage is 67%. Description mentions 'entity_id and its type' but does not explain the 'async' parameter. It adds some meaning by naming the inputs but lacks full coverage.

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 the tool returns 'audience targeting profile' as 'enrichment tags reframed as audience facets with confidence, corroboration and full provenance'. This is a specific verb+resource combination that distinguishes it from sibling tools like content_catalog or content_enrichment.

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?

Explicitly states when to use: 'an ad-tech or marketing agent needs a machine-readable, verifiable audience descriptor'. Also specifies required inputs (entity_id and type). Lacks explicit when-not-to-use or alternative tools, but the context is clear.

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

C2.8/5.0
Disambiguation2/5

Many tools have overlapping purposes, especially in competitive intelligence, ESG, and risk assessment. For example, there are multiple tools for competitor analysis (competitive_deep_dive, competitor_intel, competitor_moves, etc.) with unclear boundaries. Agents would struggle to select the correct tool without deep understanding of subtle differences.

Naming Consistency2/5

Tool names are a mix of English and French, and follow no consistent pattern. Some use snake_case (e.g., abm_architect, action_plan_esg), while others are verb-focused (e.g., content_catalog, fx_rate). The lack of a uniform naming convention makes it hard for agents to predict tool names.

Tool Count1/5

With 271 tools, the server is excessively large. Even for a broad knowledge domain, this number of tools makes discovery and selection inefficient. Typical coherent servers have 3-15 tools; this has an order of magnitude more, indicating poor scoping.

Completeness3/5

The tool set covers many domains (compliance, finance, marketing, HR, etc.), but the coverage is uneven due to redundancy. Key areas have multiple overlapping tools, while some sub-domains may still have gaps. Overall, the surface is broad but not well-curated.

Resources