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mcp-knowledge

by io.github.getgapup

ftg_opportunity_scout

Read-only

Rank the best countries for a given commodity — where the market gap, opportunity score and potential margin are highest. Cross-country scouting. When to use this tool: an agent has a commodity and needs to know WHERE to sell, export to or set up local production. Input: a commodity name.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
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 countries to return (default 20)
commodityYesCommodity name (e.g. 'rice', 'soybean', 'poultry')

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
noteNo
countYes
commodityYes
countriesYes

TDQS

A4/5.0
Behavior3/5

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

Annotations already declare readOnlyHint=true and openWorldHint=true, indicating safe read operations. The description adds that the tool ranks countries but does not disclose data sources, update frequency, or behavior for unknown commodities. It provides adequate but not rich 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.

Conciseness5/5

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

The description is three sentences, each serving a distinct purpose: stating the ranking criteria, clarifying the cross-country scope, and providing usage guidance. There is no wasted text; every sentence adds value.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness4/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given the existence of an output schema, the description does not need to explain return values. It covers purpose, usage, and input adequately for a tool with readOnlyHint and openWorldHint. A minor gap is the lack of mention of data sources or computation details, but overall it is fairly complete.

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 all three parameters (commodity, limit, async) are fully described in the input schema. The description mentions only 'commodity name', adding no new meaning beyond the schema. Baseline score of 3 is appropriate.

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 it ranks countries for a given commodity based on market gap, opportunity score, and potential margin. It uses specific verbs ('rank', 'scouting') and explicitly distinguishes itself as cross-country, differentiating it from sibling tools like ftg_market_gap or ftg_country_study.

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 description includes an explicit 'When to use this tool' section, providing clear context: when an agent needs to know where to sell, export, or set up local production for a commodity. It does not explicitly list when not to use or alternatives, but the context is sufficient for selection among many sibling tools.

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