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mcp-knowledge

by io.github.getgapup

market_research_brief

Read-only

Generate a structured, sourced market research brief on any market, sector or industry. Returns a machine-readable note with six sections: an executive overview, a market-size estimate (with assumptions and sources — no invented figures), key players, demand & technology trends, risk factors, and a traceable source list. When to use this tool: an agent needs to assess a new market, validate a business opportunity, prepare a pitch, or benchmark a sector before a strategic decision. Data is assembled live from keyless public sources: Wikipedia (sector context), World Bank (macro GDP/population for market sizing), REST Countries (geo context). Fields that cannot be sourced are marked 'unavailable' rather than estimated. Inputs: topic (required), geo and sector (optional refinements).

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
geoNoOptional geography to scope the brief (country name, region, or continent — e.g. 'France', 'Southeast Asia')
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.
topicYesMarket or sector to research (e.g. 'electric vehicle batteries', 'B2B SaaS CRM Europe', 'telemedicine Africa')
sectorNoOptional parent sector to disambiguate the topic (e.g. 'healthcare', 'energy', 'software')

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
geoYes
risksYes
topicYes
sectorYes
trendsYes
sourcesYesAll sources consulted, with URL and retrieval status
overviewYesExecutive summary of the market
key_playersYes
generated_atYesISO-8601 timestamp of generation
market_size_estimateYesMarket size estimate with hypotheses. All figures sourced or marked unavailable.

TDQS

A3.9/5.0
Behavior4/5

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

Annotations already indicate readOnlyHint and openWorldHint. The description adds valuable behavioral context: data is assembled live from keyless public sources (Wikipedia, World Bank, REST Countries) and fields that cannot be sourced are marked 'unavailable' rather than estimated. This goes beyond what annotations provide. A minor gap is not mentioning pagination or rate limits, but overall strong.

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?

The description is well-structured in two paragraphs: first explains the output structure, second covers usage and data sources. It is concise without redundant information. Each sentence contributes to understanding. Could be slightly more compact but overall efficient.

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 complexity of generating a multi-section research brief, the description adequately covers purpose, inputs, outputs, source limitations, and asynchronous execution. The presence of an output schema reduces the burden. A minor omission is no mention of result format (e.g., JSON structure) but that is likely in the output schema.

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 coverage is 100%, so the schema already documents all parameters. The description repeats the parameter names and adds usage examples (e.g., 'electric vehicle batteries') and clarifies the async behavior. This adds some value but does not significantly enhance understanding beyond the schema. Baseline 3 is appropriate.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose4/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the tool generates a 'structured, sourced market research brief' and enumerates six specific sections. The verb 'generate' and resource 'market research brief' are specific. While it could differentiate from related sibling tools like 'market_sizing' or 'competitive_deep_dive', it does not explicitly do so, preventing a score of 5.

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 provides explicit scenarios for when to use the tool: 'assess a new market, validate a business opportunity, prepare a pitch, or benchmark a sector before a strategic decision.' It also names the data sources. However, it lacks guidance on when not to use it or explicit alternatives, so it does not achieve a 5.

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.

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