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Findymail

by io.github.pipeworx-io

Findymail Find Email

findymail_find_email
Read-onlyIdempotent

Find the work email for a person by name + company domain — give a person's full name and their company domain and get their verified business email (with job title and LinkedIn when available). B2B prospecting / outreach. Example: findymail_find_email({ name: "Patrick Collison", domain: "stripe.com", _apiKey: "your-findymail-key" })

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
nameYesFull name of the person, e.g. "Patrick Collison"
domainYesCompany domain (no protocol), e.g. "stripe.com"
_apiKeyYesYour Findymail API key (get one at findymail.com)

Schema Changelog

Changes observed during successful MCP inspections. Dates show when Glama detected each change.

  1. Changed1 schema field changed
    • addedInput schema / examples
      Added value: +[
+  {
+    "_apiKey": "your-findymail-api-key",
+    "domain": "stripe.com",
+    "name": "Patrick Collison"
+  },
+  {
+    "_apiKey": "your-findymail-api-key",
+    "domain": "google.com",
+    "name": "Sarah Chen"
+  }
+]
  2. First observed

TDQS

A4.6/5.0
Behavior5/5

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

Annotations already indicate read-only, idempotent, non-destructive. Description adds details about output (verified business email, job title, LinkedIn) which enriches behavioral understanding without contradiction.

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?

Two sentences plus an example, all essential and front-loaded. No redundant information.

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?

Description covers purpose, inputs, and output format. While no output schema, the description hints at return fields. Missing details on error handling or rate limits, but overall adequate for a simple lookup tool.

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 baseline is 3. Description adds an example with actual values and clarifies domain format ('no protocol'), providing additional context beyond the schema descriptions.

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?

Description clearly states the tool finds work emails for a person given name and company domain, with specific output including job title and LinkedIn. It distinguishes itself from the sibling 'findymail_reverse_email' implicitly by focusing on forward lookup.

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?

Description provides context 'B2B prospecting / outreach' and an example, making usage clear. However, it does not explicitly contrast with the sibling tool 'findymail_reverse_email' or mention when not to use it.

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

A3.8/5.0
Disambiguation2/5

Several tools have heavily overlapping purposes: ask_pipeworx and ask_pipeworx_beta are currently identical, deep_research/validate_claim/ask_pipeworx all route factual questions to the same underlying catalog, and ai_visibility_check is essentially wrapped by scan_competitor_ai_presence. The long descriptions help, but the boundaries between query, research, and verification tools are genuinely ambiguous.

Naming Consistency2/5

Names are all lowercase snake_case, but there is no consistent verb_noun or domain pattern: ask_pipeworx, findymail_find_email, scan_competitor_ai_presence, polymarket_kalshi_spread, and generate_llms_txt each use a different structural convention. The mix of brand prefixes, domain prefixes, and bare commands makes the naming feel ad hoc rather than systematic.

Tool Count2/5

33 tools is well over the 25+ threshold and reflects a server that bundles at least five distinct concerns: email lookup, structured data research, prediction-market analysis, subscriptions, and memory. Most individual tools earn their place, but the count is too high for coherent tool selection in a single MCP server.

Completeness3/5

Coverage is broad and mostly self-sufficient for the Pipeworx data ecosystem: querying, deep research, entity resolution, comparisons, verification, subscriptions, memory, and one-off utilities are all present. There are notable gaps though—there is no tool to fetch a full record from a returned pipeworx:// citation URI, and the Findymail side is limited to find/reverse with no verification or bulk capability.

Resources