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Startvest-LLC

IdeaLift MCP Server

by Startvest-LLC
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TypeScript
Hybrid

query_ideas

Read-only

Execute natural language queries to analyze workspace ideas, track product feedback, and identify high-impact features using RICE scoring and aggregated signals.

Instructions

Execute a natural language query about ideas in the workspace. Examples:

  • "Show me high-impact features from enterprise customers"

  • "What are the top 5 most requested features?"

  • "Find bugs reported this month"

  • "Show ideas with the highest RICE scores"

  • "What feedback has come from Slack in the last week?"

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
queryYesNatural language query about ideas

Implementation Reference

  • src/mcp/tools/proxyTools.ts:165-183 (schema)
    The tool named "query_ideas" is defined here in the proxyTools.ts list of tools.
      {
    name: 'query_ideas',
    description: `Execute a natural language query about ideas in the workspace.
Examples:
- "Show me high-impact features from enterprise customers"
- "What are the top 5 most requested features?"
- "Find bugs reported this month"
- "Show ideas with the highest RICE scores"
- "What feedback has come from Slack in the last week?"`,
    inputSchema: {
      type: 'object' as const,
      properties: {
        query: { type: 'string', description: 'Natural language query about ideas' },
      },
      required: ['query'],
    },
    annotations: { readOnlyHint: true, destructiveHint: false, openWorldHint: true },
    _meta: { 'openai/visibility': 'public' },
  },

Tool Definition Quality

A3.5/5.0
Behavior3/5

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

Annotations declare readOnlyHint=true and openWorldHint=true. The description adds valuable behavioral context through examples showing the system's ability to interpret complex natural language filters (RICE scores, Slack sources), which aligns with the openWorldHint. However, it omits details about result limits, pagination, or ambiguity handling.

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?

Front-loaded purpose statement ('Execute a natural language query...') followed immediately by a bulleted example list. No wasted words; structure efficiently communicates both capability and input patterns in minimal space.

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?

Adequate for a single-parameter read operation. Examples compensate for lack of output schema by demonstrating expected query complexity. Annotations cover safety profile. Minor gap: no mention of result format or volume limitations.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters4/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

With 100% schema coverage, the baseline is 3. The description significantly exceeds this by providing five diverse examples that illustrate the parameter's semantic range—showing it accepts aggregations ('top 5'), temporal filters ('this month'), source filtering ('Slack'), and scoring metrics ('RICE scores').

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?

States specific action (Execute) and resource (natural language query about ideas). Examples clarify scope (filtering by impact, scores, timeframe). However, it does not explicitly distinguish from sibling 'search_ideas' or 'list_ideas', which likely perform similar retrieval with structured parameters.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines2/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

Provides five concrete input examples but offers no explicit guidance on when to use this tool versus structured alternatives like 'search_ideas', 'list_ideas', or 'get_idea'. No prerequisites, error conditions, or exclusions are mentioned.

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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