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Findymail

by io.github.pipeworx-io

Findymail Reverse Email

findymail_reverse_email
Read-onlyIdempotent

Look up the person/LinkedIn profile behind an email address — give an email and get the person's name, company, job title, and LinkedIn URL when available. Reverse-email enrichment. Example: findymail_reverse_email({ email: "patrick@stripe.com", _apiKey: "your-findymail-key" })

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
emailYesEmail address to look up, e.g. "patrick@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",
+    "email": "patrick@stripe.com"
+  },
+  {
+    "_apiKey": "your-findymail-api-key",
+    "email": "sarah.chen@google.com"
+  }
+]
  2. First observed

TDQS

A4.3/5.0
Behavior3/5

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

Annotations already declare readOnlyHint=true, openWorldHint=true, idempotentHint=true, and destructiveHint=false. The description adds minimal behavioral context beyond the purpose (e.g., it does not mention rate limits, auth requirements beyond the API key parameter, or that results may be incomplete). It does not contradict annotations.

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: two sentences that immediately state the action and outputs, followed by an example. Every part adds value, and there is no unnecessary text.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness5/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given the tool's simplicity (2 parameters, no output schema, no nested objects), the description covers what is needed. It states input, output, and provides an example. No missing context given the tool's complexity.

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 schema already describes both parameters. The description adds an example call with realistic values, which helps clarify how to use the parameters. This addition slightly improves understanding beyond the schema's 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?

The description clearly states the tool's purpose: 'Look up the person/LinkedIn profile behind an email address'. It specifies the input (email) and outputs (name, company, job title, LinkedIn URL). The verb 'Look up' and resource 'person/LinkedIn profile' are specific. The tool name includes 'reverse_email', which contrasts with the sibling 'findymail_find_email', providing implicit distinction.

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 includes an example call and explains what the tool does. However, it does not explicitly state when to use this tool over its sibling 'findymail_find_email' or other tools. The naming implies the direction, but explicit guidance would be clearer.

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

All Findymail toolsInspect
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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