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Handflow

by io.handflow
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sign_up_to_earn

Sign a person up to earn money doing real-world tasks with Handflow (starting in New York City). They set their own price and hours. Handflow emails them when there is work that fits.

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": {
      "name": "<name>",
      "email": "<email>",
      "city": "<city>",
      "details": "<details>"
    },
    "name": "sign_up_to_earn"
  }
}

Input Schema

NameRequiredDescriptionDefault
cityYesCity or neighborhood where they can work
langNoLanguage for our emails. Defaults to "en".
nameYesThe person's name
emailYesEmail where Handflow should write to them
detailsYesWhat they can do: skills, tools and availability

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observed

TDQS

A3.5/5.0
Behavior3/5

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

With no annotations at all, the description must carry the full behavioral burden. It does disclose a concrete post-call side effect — Handflow emails the person when matching work appears — which is genuinely useful. But it says nothing about duplicate signups with the same email, what response the caller gets, or consent/PII handling for the collected name and email.

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?

Three short sentences that front-load the action and scope before the follow-up behavior. No filler, no restating of the tool name, and the New York City qualifier is placed where it is read first.

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

Completeness3/5

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

For a 5-parameter PII-collecting mutation with no annotations and no output schema, the description covers what the tool does and the downstream email, but omits duplicate handling, expected response, and any consent or privacy note. An agent can call it, but cannot predict edge-case outcomes.

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 100%, so the baseline is 3 and the schema already explains name, email, city, lang, and details. The description's only added semantic is 'They set their own price and hours,' which has no corresponding parameter and loosely maps onto details, adding more product framing than parameter meaning.

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?

The description opens with a specific verb and resource: signing a person up to earn money doing Handflow tasks, with a geographic scope (starting in New York City). An agent can tell this creates a worker signup, but nothing distinguishes it from the sibling request_early_access, so it falls short of a 5.

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

Usage Guidelines3/5

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

Usage is implied by the recruitment framing (a person wants to earn money doing tasks), but there is no explicit when-to-use or when-not guidance and no pointer to the sibling request_early_access as an alternative path. It is adequate but leaves routing to inference.

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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