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Misar-AI

MisarReach MCP Server

discover_companies

Read-onlyIdempotent

Identify companies by industry, location, headcount, or tech stack, with optional contact email retrieval via Hunter.io.

Instructions

Find COMPANIES matching firmographic criteria via Hunter.io, optionally pulling contact emails for each.

This is company-level discovery — reach for it when the user is targeting organisations by industry, location, headcount or tech stack. When they want named people, use search_leads. Filters combine with AND, so stacking many narrows results sharply.

Requires an API key. Setting fetch_emails=true performs email lookups and COSTS CREDITS; leaving it false is a plain company search. Returns matching companies with firmographics. No lead is saved to the account by this call.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
limitNoMax companies to return (1-100, default 20)
queryNoFree-text company search query
industryNoIndustry filters (max 10)
locationNoLocation filters (max 10)
technologyNoTechnology stack filters (max 10)
fetch_emailsNoAlso fetch contact emails for discovered companies (default false)
headcount_maxNoMaximum employee count
headcount_minNoMinimum employee count
Behavior5/5

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

Annotations already declare readOnlyHint and destructiveHint, but the description adds valuable context: 'Requires an API key', 'fetch_emails=true performs email lookups and COSTS CREDITS', and 'No lead is saved to the account by this call.' These disclosures about side effects and resource usage exceed what annotations provide.

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 and well-organized: a one-sentence summary, followed by usage context and important caveats (API key, credits, side-effect-free). No unnecessary words or repetition.

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?

For a tool with 8 parameters and no output schema, the description covers key aspects: purpose, alternatives, behavior with fetch_emails, and side-effect guarantees. It might have detailed error scenarios or result format, but it is complete enough for an agent to use effectively.

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?

Schema coverage is 100%, so the baseline is 3. The description adds meaning by explaining that 'filters combine with AND' and that fetch_emails has cost implications, which is not evident from the schema alone. This adds semantic depth to parameters beyond their type definitions.

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 'Find COMPANIES matching firmographic criteria via Hunter.io, optionally pulling contact emails for each.' It uses a specific verb and resource, and explicitly contrasts with sibling search_leads ('When they want named people, use search_leads'), distinguishing it from alternatives.

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?

It provides explicit when-to-use guidance: 'reach for it when the user is targeting organisations by industry, location, headcount or tech stack.' It also names an alternative ('use search_leads') and explains the AND behavior of filters, giving clear context for when to choose this tool over others.

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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