gapup-mcp

content_enrichment

Read-only

Return the enriched tag profile of a content entity — the Gapup moat. Each tag carries a facet (genre, theme, play-mode, perspective…), a confidence score, a corroboration score and its full provenance (which sources corroborated it, when). The response also carries an entity-level provenance block (average confidence, data freshness). When to use this tool: an agent has a franchise or work id (from content_catalog) and needs a fine-grained, machine-readable, verifiable characterisation for matching, recommendation, contextual targeting or analysis. Inputs: an entity id and its type.

Input Schema

TableJSON Schema

Name	Required	Description
`async`	No	If 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_id`	Yes	Entity id from content_catalog (e.g. 'music-daft-punk', 'film-the-dark-knight-collection:the-dark-knight')
`entity_type`	No	Whether the id is a franchise or a work (default franchise)

Output Schema

TableJSON Schema

Name	Required	Description
`tags`	Yes
`entity_id`	Yes
`tag_count`	Yes
`provenance`	Yes	Entity-level trust & freshness summary.
`entity_type`	No

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 by detailing the output structure (tag profile with facet, scores, provenance) and entity-level provenance. No contradictions; it enhances transparency beyond 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?

Description is concise (3–4 sentences) with front-loaded purpose. It efficiently covers purpose, output structure, use case, and inputs without redundancy. Every sentence adds value.

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?

Given the tool has 3 parameters and an output schema, the description is complete. It explains the tool's purpose, what the output contains, when to use it, and inputs. The existence of an output schema reduces the need to detail every field.

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 100%, so description adds limited value beyond schema descriptions. It mentions entity_id comes from content_catalog and notes entity_type default, but these are already in schema. No additional parameter semantics are provided.

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?

Description clearly states it returns an enriched tag profile of a content entity with specific components (facet, confidence, corroboration, provenance). It explicitly mentions the use case (having a franchise/work id from content_catalog) and differentiates from siblings by focusing on fine-grained, machine-readable characterization.

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 'When to use this tool' section provides specific conditions (entity id from content_catalog, need for characterization) and lists example applications. It does not explicitly state when not to use or name alternatives, but the guidance is clear 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

C2.5/5.0

Disambiguation2/5

With 271 tools, many have overlapping purposes (e.g., multiple competitor intel tools, multiple financial modelers, multiple ESG auditors). Detailed descriptions help slightly, but the sheer volume creates confusion. Agents would struggle to select the right tool among many similar options.

Naming Consistency1/5

Tool names are wildly inconsistent: mix of English and French, snake_case and short phrases, some very generic (process, run, execute equivalents). No discernible naming convention (e.g., abm_architect vs. boundary_control vs. bp_narratif). This makes it hard to predict tool names.

Tool Count1/5

271 tools is far beyond typical well-scoped servers (3-15). This indicates an unfocused, over-bloated tool surface. Even for a general business intelligence server, this number is excessive and violates the principle of each tool earning its place.

Completeness2/5

Despite the large count, coverage feels scattered. Some domains (e.g., content, competitive intel) have many tools, while others (e.g., supply chain, HR) have gaps. The set lacks a coherent scope; it seems like a dump of many separate tool collections rather than a complete, curated surface.

Resources

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