gapup-mcp

content_compare

Read-only

Compare the tag profiles of two content entities (franchises or works) and measure how similar they are. Returns a Jaccard similarity score, the list of shared tags, the tags unique to each entity, and a breakdown of shared tags by facet. When to use this tool: an agent needs to compare two franchises or works (e.g. 'how similar are Dark Souls and Elden Ring?', 'what do Street Fighter and Mortal Kombat have in common?', 'on which axes do these two games differ?'), find positioning overlap, identify cross-sell opportunities, or answer 'if you liked X you might like Y' questions backed by data. Works for any domain (video-games, music, film, tv).

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_a`	Yes	Id of the first entity from content_catalog (e.g. 'game-dark-souls', 'music-daft-punk').
`entity_b`	Yes	Id of the second entity from content_catalog (e.g. 'game-elden-ring', 'music-justice').
`entity_type`	No	Whether both ids are franchises or works (applies to both). Defaults to 'franchise'.

Output Schema

TableJSON Schema

Name	Required	Description
`entity_a`	Yes
`entity_b`	Yes
`similarity`	Yes	Jaccard index = \|shared\| / \|union\|, rounded to 2 decimal places. 0 = no overlap, 1 = identical profiles.
`a_tag_count`	Yes
`b_tag_count`	Yes
`entity_type`	Yes
`shared_tags`	Yes	Tags present in both entities (up to 40).
`unique_to_a`	Yes	Tags present only in entity_a (up to 40).
`unique_to_b`	Yes	Tags present only in entity_b (up to 40).
`shared_count`	Yes
`shared_by_facet`	Yes	Count of shared tags per facet (e.g. { genre: 3, theme: 5 }). Shows which dimensions drive the similarity.

TDQS

A4.4/5.0

Behavior4/5

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

Annotations indicate readOnlyHint=true and openWorldHint=true. Description details the return structure (Jaccard, shared tags, etc.) and explains async behavior. No contradictions; adds behavioral context 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.

Conciseness4/5

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

Single concise paragraph with clear separation of purpose and usage guidelines. No wasted sentences, though slightly longer than minimal. Well-structured and front-loaded.

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?

Comprehensive coverage: purpose, output, usage scenarios, parameter context, and domain generality. Output schema exists but description still explains return values, making it self-contained.

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

Parameters4/5

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

Schema coverage is 100% with descriptions for each parameter. Description adds value by giving concrete ID examples (e.g., 'game-dark-souls') and explaining async parameter usage with polling reference.

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 the tool compares tag profiles of two content entities and measures similarity, listing specific outputs (Jaccard score, shared/unique tags, facet breakdown). It distinguishes from siblings like content_similar by specifying comparison of two entities.

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?

Provides explicit 'When to use' section with concrete examples (comparing franchises/works, finding overlap, cross-sell, recommendation) and states domain agnosticism. Lacks explicit when-not-to-use or alternative tool names.

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.

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