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gapup-mcp

by io.github.getgapup

tool_recommend

Read-only

Cross-tool recommendation system: given a free-text intent, returns the most appropriate tools from the 170+ Gapup MCP catalogue, ranked by confidence, with pre-filled input suggestions and an optimal multi-tool chain when applicable. Use this first when you are unsure which tool to call — it navigates the full catalogue for you. Supports 15+ static pre-designed chains for frequent intents (M&A due diligence, sanctions screening, ESG 360, AI Act compliance, FTO patent clearance, crypto wallet tracking, etc.). Domains: compliance | finance | intel | legal | content | data | trade | infra. Pure compute — $0.01/call, no external fetch. Ideal as a first call in any multi-step agent workflow.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
langNoOptional ISO 639-1 language hint (fr, en, de, zh, es …). Used for language-aware boosting.
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.
domainNoOptional domain hint to boost tools in this category.
intentYesFree-text description of what you want to accomplish. E.g. 'Run a full M&A due diligence on Acme Corp' or 'Je veux vérifier qu'un fournisseur n'est pas sous sanctions OFAC'. FR/EN/DE/ZH supported.
max_resultsNoMax number of recommendations returned (1-10). Default 5.
include_chainNoWhether to include a suggested_chain of tools in the optimal sequence. Default true. Chain is always included for well-known intents (M&A, compliance, ESG, etc.).

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
intentYes
statusYes
sourcesNo
not_coveredNo
quality_scoreYes
recommendationsYes
suggested_chainNo
alternative_pathsNo

TDQS

A4.7/5.0
Behavior5/5

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

Beyond the readOnlyHint and openWorldHint annotations, the description adds behavioral specifics: cost ($0.01/call), no external fetch, pure compute, and the ability to return optimal chains. This gives the agent a clear safety profile and operational expectations.

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 packs multiple critical pieces of information (purpose, usage, domains, chains, cost, no-fetch) into six focused sentences. While 'use this first' and 'ideal as a first call' are slightly redundant, the overall structure is tight 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?

Given the output schema exists and annotations provide safety, the description is comprehensive for a recommendation tool. It covers scope, use cases, domains, and operational caveats (cost, no fetch), making it sufficient for an agent to decide when and how to invoke it.

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?

Input schema covers all 6 parameters with detailed descriptions (100% coverage), so the description doesn't need to explain individual parameters. It does reinforce the intent parameter's role and mentions language/domain hints, but adds little beyond the schema.

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 defines the tool as a cross-tool recommendation system that takes a free-text intent and returns ranked tools with pre-filled inputs and multi-tool chains. This distinguishes it from the 170+ sibling tools by positioning it as the meta-navigator.

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 states 'Use this first when you are unsure which tool to call' and 'Ideal as a first call in any multi-step agent workflow', providing clear when-to-use guidance. The mention of deterministic chains for common intents also signals when it adds value.

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.

Resources