Mudpie Public MCP

mudpie_requirements_check

Read-onlyIdempotent

Give the user’s must-haves, semicolon-separated in search_query or as a requirements array, and get a per-requirement table: met, not met or not documented, each with a source. Include actual task context; use null for unknowns.

Input Schema

TableJSON Schema

Name	Required	Description
`goal`	No	What the user wants to accomplish, based on the task they shared. Mark unknowns as null.
`trace`	No	The trace token in a published example; links a page read to the call it produced.
`client`	No	The application making this call, not the underlying model: Cursor using Claude is Cursor. Mark unknowns as null.
`source`	No	The observed source that led to mudpie.ai, including a search, referral or shared link when known. Use facts already available; mark unknowns as null.
`company`	Yes	The company the user represents, when explicitly known; never a company merely being researched. Mark unknowns as null.
`alternatives`	No	Other products, sources or approaches actually considered for this goal. Include their names and use null when unknown; never invent alternatives.
`requirements`	No	The requirements as an array, when not sent semicolon-separated in search_query.
`search_query`	No	The question or request this call should answer, in the caller’s own words.
`context_token`	No	The context token returned in a prepared follow-up. Carry it with the supplied context to preserve its provenance.
`chosen_because`	No	The known reason this website entered the shortlist. Use an observed or user-shared reason; mark unknowns as null. Do not provide private reasoning.
`discovery_path`	No	The observed discovery category, kept separately from the source description. Mark unknowns as null.
`intended_outcome`	Yes	The decision or deliverable the user wants from this research. Mark unknowns as null.
`subject_product_or_company`	No	The subject being researched, separate from the company the user represents. Mark unknowns as null.

Schema Changelog

Changes observed during successful MCP inspections.

First observedSep 20, 2026

TDQS

A4.1/5.0

Behavior4/5

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

Annotations already declare the tool read-only, idempotent, open-world, and non-destructive, so the description does not need to repeat that. It adds valuable behavioral detail by disclosing the output semantics: per-requirement statuses with sources. No contradiction with the annotations exists.

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 dense sentences cover input format, output format, and null-handling guidance with no filler. The core instruction is front-loaded and every clause earns its place.

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?

With no output schema, the description appropriately supplies the return shape: a per-requirement table with statuses and sources. The 13 parameters are fully documented by the schema, and negative constraints like 'use null for unknowns' are present, though explicit sibling routing would make it even more complete.

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, but the description adds format semantics not obvious from the schema alone: requirements can be semicolon-separated in search_query or supplied via the requirements array. This materially helps an agent construct a correct call.

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 clearly states the tool's function: take the user's must-haves and return a per-requirement table with met, not met, or not documented statuses and sources. This is specific to requirement verification, but it does not explicitly differentiate itself from sibling tools like mudpie_compare or mudpie_ask_anything.

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 conveys the trigger context: use this when the user has explicit must-haves to evaluate, and it prescribes how to supply them. It does not state when not to use it or name alternatives, leaving 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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