Generate text from a Markov chain trained on your input corpus, using word- or character-level tokens with configurable order and output length.
Generate text using a Markov chain trained on input text (word or character level).
| Name | Required | Description | Default |
|---|---|---|---|
| mode | No | 'word' (default) or 'character'. | |
| text | Yes | Training corpus. | |
| order | No | Chain order/context size (default 2). | |
| length | No | Number of tokens to generate (default 50). |
Changes observed during successful MCP inspections.
v0.1.11Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries the full behavioral burden but only describes the generation mechanism. It does not disclose whether output is random or deterministic, whether results are reproducible, what the return value looks like, or any performance or side-effect considerations.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, front-loaded sentence with zero wasted words. It efficiently communicates the core purpose and scope.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a simple generation tool with a fully documented schema and no output schema, the description covers the core purpose but omits useful context such as the output format or randomness behavior. It is adequate but incomplete.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100%, so all four parameters are documented in the schema. The description mentions the mode (word or character level) and training corpus, but adds no meaning beyond what the schema already provides for order and length.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description uses a specific verb ("Generate") and resource ("text using a Markov chain trained on input text") with scope (word or character level). It clearly distinguishes this tool from other text-generation siblings by naming the unique mechanism, so an agent can tell what it does without opening the schema.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description states what the tool does but provides no guidance on when to use it versus alternatives, no prerequisites, and no exclusions. It leaves the agent to infer usage solely from the purpose statement.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.