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gapup-mcp

by io.github.getgapup

content_discovery

Read-only

Discover content franchises within a domain. Two modes: pass tag for a precise taxonomy match (every game tagged 'co-op'), or pass query for free-text SEMANTIC search powered by pgvector embeddings — finding franchises by meaning ('dark atmospheric games about isolation') even when no literal tag matches. Results are verifiable: tag mode carries tag confidence/corroboration, semantic mode carries a similarity score; both carry entity freshness. When to use: an agent wants a domain-scoped shortlist by tag or by intent. Inputs: a domain plus either a tag or a free-text query.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
tagNoTag label to match precisely (e.g. 'thriller', 'co-op'). Mutually exclusive with `query`.
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.
limitNoMaximum franchises to return (default 25)
queryNoFree-text intent for semantic search (e.g. 'melancholic synth-pop about heartbreak'). Mutually exclusive with `tag`.
domainYesContent domain to search within

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
tagNo
countYes
queryNo
domainYes
methodYes
franchisesYes

TDQS

A5/5.0
Behavior5/5

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

Beyond the readOnlyHint annotation, the description adds valuable behavioral context: results are 'verifiable' with confidence/similarity scores and entity freshness, and the two modes have distinct output characteristics. This informs the agent about what to expect without needing to invoke the tool.

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?

The description is well-structured and front-loaded: purpose first, then modes, then result characteristics, then use case. Every sentence contributes meaningful information without redundancy or fluff. Length is appropriate for the tool's complexity.

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 schema covers all parameters and an output schema exists, the description provides the missing conceptual layer: when to use which mode, what to expect in results, and how to think about the domain scoping. It is complete for making an informed tool selection.

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

Parameters5/5

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

While schema coverage is 100%, the description enriches parameter meaning significantly: it clarifies the tag/query mutual exclusivity, explains the semantic search mechanism (pgvector embeddings), and frames outputs in terms of 'domain-scoped shortlist.' This adds depth beyond the schema field descriptions.

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's purpose: 'Discover content franchises within a domain.' It uses a specific verb (discover) and resource (content franchises), and immediately distinguishes the two modes (tag vs query), which separates it from sibling tools like content_taxonomy and content_similar.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines5/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

Explicitly provides 'When to use:' guidance and explains which mode fits which intent (precise taxonomy match vs free-text semantic search). This gives clear direction for selecting the tool and its parameters, going beyond a simple statement of functionality.

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

C2.4/5.0
Disambiguation1/5

Over 50 tools share the identical template 'Gapup agent-payable C-suite expertise' with similar French descriptions and reference cases, making their boundaries indistinguishable. Clusters like competitor_intel, competitive_deep_dive, competitor_moves, competitor_profiles, competitor_pricing_radar, competitor_pricing_scrape, and competitor_recommendations heavily overlap in purpose.

Naming Consistency1/5

Names are chaotic: mix of French and English, snake_case and camelCase, verb_noun, noun, and adjective forms with no uniform pattern. Examples like 'bp_narratif', 'content_enrichment', 'ai_governance_full_report_async', and 'job_result' show no coherent naming convention.

Tool Count1/5

271 tools is far beyond any reasonable MCP server scope, creating an overwhelming selection burden for agents. This count vastly exceeds the 25+ threshold for 'too many' and makes navigation impractical.

Completeness2/5

While the server covers many business domains, it lacks lifecycle operations (e.g., no update/delete tools for the deliverables it generates) and the input specifications are vague ('documented case fields' without documentation), creating functional dead ends. The sheer breadth does not compensate for these gaps.

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