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mcp-knowledge

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

A4.5/5.0
Behavior4/5

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

Beyond annotations (readOnlyHint, openWorldHint), the description adds important behavioral context: 'Pure compute — $0.01/call, no external fetch', mentions async mode, and references pre-designed chains. It could be improved by noting any limitations, but adds significant value.

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 concise, well-structured, and front-loaded with the core functionality. Every sentence adds value, covering purpose, usage, domains, cost, and chains without extraneous text.

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's complexity (170+ tools, domains, chains, async, output schema), the description is remarkably complete. It covers when to use, domains, cost, async behavior, and hints at output features, leaving no major gaps.

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% with good descriptions for each parameter. The description does not add new parameter semantics beyond what the schema provides; it reinforces the intent parameter's role but doesn't explain parameters like lang, async, domain, max_results, include_chain in more depth.

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: a cross-tool recommendation system that returns appropriate tools from a catalogue given a free-text intent. It distinguishes itself from siblings by being the go-to tool for navigating the full set of tools.

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 advises '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 and implying alternatives (other tools) when the intent is known.

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