freshcontext-mcp

extract_landscape

Read-only

Analyze competitive landscapes by querying 6 sources including YC startups, GitHub, and Product Hunt to identify who's building similar projects, funding status, and traction.

Instructions

Composite intelligence tool. Given a project idea or keyword, simultaneously queries YC startups, GitHub repos, HN, Reddit, Product Hunt, and package registries to answer: Who is building this? Is it funded? What's getting traction? Returns a unified 6-source timestamped landscape report.

Tool call template

Replace each placeholder with a value matching the input schema. Only required parameters are included; add optional parameters as needed.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {
      "topic": "<topic>"
    },
    "name": "extract_landscape"
  }
}

Input Schema

Name	Required	Description	Default
`topic`	Yes	Your project idea or keyword e.g. 'mcp server' or 'cashflow prediction'
`max_length`	No

Schema Changelog

Changes observed during successful MCP inspections.

First observedv0.3.12Mar 21, 2026

TDQS

A4.2/5.0

Behavior4/5

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

Annotations indicate readOnlyHint=true and openWorldHint=true, which the description aligns with by describing a querying operation without implying mutations. The description adds valuable context beyond annotations: it specifies the 6 sources queried, the types of questions answered, and that it returns a 'unified 6-source timestamped landscape report,' which helps the agent understand the tool's scope and output format.

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 front-loaded and efficient: it starts with the tool's composite nature, lists the sources, specifies the questions answered, and describes the output in a single, well-structured sentence. Every part earns its place without redundancy.

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 tool's complexity (querying 6 sources) and lack of output schema, the description does a good job of explaining what the tool does and what it returns. However, it could be more complete by detailing the report structure or any limitations (e.g., rate limits, data freshness). With annotations covering safety and openness, the description is largely adequate but has minor 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 description coverage is 50% (one of two parameters has a description). The description mentions 'project idea or keyword,' which aligns with the 'topic' parameter but doesn't add details beyond the schema's description. It doesn't mention the 'max_length' parameter at all. Since schema coverage is moderate, the description provides minimal additional parameter semantics, meeting the baseline.

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: it's a 'composite intelligence tool' that 'simultaneously queries YC startups, GitHub repos, HN, Reddit, Product Hunt, and package registries' to answer specific questions about who is building something, funding status, and traction. It distinguishes itself from siblings by specifying the 6 sources it queries and the unified report it returns, unlike more focused sibling tools like extract_yc or extract_github.

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 clear context for when to use this tool: 'Given a project idea or keyword' to get a comprehensive landscape report. It implies usage for broad research rather than specific source queries, but it doesn't explicitly state when not to use it or name alternatives among the sibling tools (e.g., using extract_yc for YC-only data).

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.