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Will It Stick

Make it stick

make_it_stick
Read-only

Hand it a draft and its goal; get back the version that sticks. The tool scans the draft for its real point (every part of it, human stakes first), then writes three transformations: a CONCRETE COMPARISON (the key fact restated as something the reader can see, the popcorn move), a CORE COMPRESSION (the one line the reader must repeat, like 'School is cancelled'), and a SCENE (one concrete moment built only from what the draft holds). A code fact gate drops any transformation whose numbers are not stated in the draft or a plain derivation of stated numbers; nothing is ever invented. Then cold readers pick the winner by a gist test: each candidate is read cold, the reader's one-line gist is scored against the point and the key fact, and the best-carrying version wins. The author's ORIGINAL competes as a candidate and can win outright: status 'already_sticks' means the words already stick and the original is the version to send. If the scan finds the core is unreachable without inventing (the draft needs a name, a scene, or an identity line only the author holds), the first result has status 'questions_first' with up to three short questions; pass the author's answers back as answers on a second call, or set skip_questions true to transform the words as they stand. The result returns the winning version with its kind label, the original alongside, the losing transformations as alternates, and what a cold reader carried away from the winner. Takes about half a minute: cold readers are not instant. Use check_if_it_sticks instead when the human wants the diagnosis and scores first. Your words are not recorded or kept; the server keeps only daily counts of how many runs happened (integers, no text). The readers run on Cloudflare Workers AI (Meta Llama 3.3 70B); under Cloudflare's published Workers AI terms, your text is not retained once it is no longer needed to answer, and it is never used to train AI models.

Input Schema

NameRequiredDescriptionDefault
goalYesOne line: what should these words do? Who should do, feel, or remember what? Example: 'Get parents to thank a teacher this week.'
textYesThe draft to transform, in the user's own words: a message, note, toast, post, or pitch. A few sentences up to about 900 words.
answersNoAnswers to a previous questions_first result, in the author's own words. Pass the same text and goal, plus these, to run the transformation with the seed material included.
audienceNoOptional: who it is for, named plainly, not 'everyone'. Example: 'my team at work'.
skip_questionsNoSet true when the author declined the seed questions: transforms the words as they stand.

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observed

TDQS

A4.8/5.0
Behavior5/5

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

Beyond the readOnly and non-destructive annotations, the description discloses the internal workflow: fact gating, cold-reader scoring, original-as-candidate comparison, possible statuses, runtime, data-retention limits, and the model provider. It gives the agent enough behavioral context to set expectations and handle the multi-step flow.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness4/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is front-loaded with the core promise and then explains the transformation pipeline, statuses, and privacy terms. It is longer than necessary for a simple tool, but the complexity and multi-step workflow justify most sentences; a few examples and asides could be trimmed.

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?

There is no output schema, so the description must explain returns, and it does: winning version, kind label, original, alternates, cold-reader takeaway, and the already_sticks and questions_first statuses. Given the tool's complexity, the definition is complete enough for an agent 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 already 100%, so the schema documents each parameter. The description adds meaningful semantics for answers and skip_questions, explaining that answers seed a second call after a questions_first result and that skip_questions transforms the draft as-is. It adds less for text and goal, but overall it goes beyond the schema.

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 opens with a specific verb-and-resource transformation: hand it a draft and goal, receive the version that sticks. It also names the sibling tool check_if_it_sticks and explains the different use case, so the agent can distinguish them without opening either schema.

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?

It explicitly routes the agent: use this tool for transformation, use check_if_it_sticks when the human wants diagnosis and scores first. It also explains the questions_first follow-up path and skip_questions behavior, so the agent knows how to handle each outcome.

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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