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TutorPing

Ask TutorPing to find a tutor

request_tutor

Submit a request for TutorPing to match a student with a tutor, when search_tutors finds no fit or the user prefers to be matched. TutorPing replies by email within 1 business day with tutor options. Collect what the student needs, then confirm the details AND get the user's explicit agreement that TutorPing may share the request and contact details with matching tutors before calling. Ask only for a first name, never a child's full name.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
emailYesWhere TutorPing should send tutor options
levelNoe.g. "11th grade", "college", "adult beginner"
notesNoGoals, current scores, anything a tutor should know
phoneNo
onlineNotrue if online sessions are fine
consentYesThe user agreed TutorPing may share this request and contact details with matching tutors
subjectYese.g. "SAT math", "AP Chemistry", "piano"
locationNoCity or area for in-person; omit if online only
scheduleNoe.g. "weekday evenings, test on Dec 6"
budgetPerHourNoUSD per hour
studentFirstNameNo

Schema Changelog

Changes observed during successful MCP inspections.

  1. Added

TDQS

A4.7/5.0
Behavior4/5

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

Annotations only declare openWorldHint/destructiveHint, so the description carries most of the burden and does well: 1-business-day email reply, an external party (TutorPing) receiving data, and a hard consent precondition. The one real gap is that it doesn't say the call fails without consent, though the schema's const:true hints at it.

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?

Four sentences that are front-loaded with the action, then the trigger, then the turnaround, then the mandatory pre-call steps and privacy rule. No filler; each sentence adds a distinct operating constraint.

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?

For an 11-parameter, no-output-schema, write-to-external-service tool, the description supplies the process, the timing expectation, the consent gate, and the privacy constraint. An agent has everything needed to invoke it correctly.

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 82% (baseline 3), and the description adds genuine meaning: consent is framed as the user's explicit agreement to share the request and contact details, and studentFirstName is constrained to a first name only. It doesn't clarify level/notes/schedule fields beyond the schema, so it sits slightly above baseline rather than at 5.

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?

Specific verb+resource ('Submit a request for TutorPing to match a student with a tutor') with the outcome named. It is immediately distinguishable from siblings search_tutors (search) and request_booking (booking a specific tutor).

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?

States the exact trigger condition: when search_tutors finds no fit or the user prefers to be matched, routing the agent from the sibling search tool to this one. It also specifies the pre-call workflow — collect needs, confirm details, obtain explicit consent.

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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