Author and validate Calaf workspace seeds against the app's real importer. No account needed.
Returns the complete specification (markdown) for calaf_seed: 1 JSON documents — the format to follow when a user asks for a Calaf seed. Read it before authoring; then check your draft with validate_seed.
| Name | Required | Description | Default |
|---|---|---|---|
No parameters | |||
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint=true, so the safety profile is known. The description adds behavioral detail by specifying the output is markdown and that it serves as the authoritative format reference, going beyond the annotation.
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 two concise sentences, front-loaded with the core purpose and followed by usage direction. Every phrase earns its place, with no redundancy or filler.
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?
Given the tool's simplicity (no parameters, clear output type), the description fully covers what the tool does, when to use it, and how it relates to the sibling. The reference to validate_seed completes the workflow.
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?
The tool has zero parameters, so per the rubric the baseline is 4. The description mentions no parameter details, but none are needed since there are none to document.
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 explicitly states the tool returns the complete specification (markdown) for calaf_seed, a specific verb and resource. It differentiates from the sibling validate_seed by indicating it is for obtaining the format, while validation is a separate step.
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?
Provides explicit when-to-use guidance: 'Read it before authoring; then check your draft with validate_seed.' This clearly directs the agent on sequencing and names the alternative tool, leaving no ambiguity about when to invoke this vs validate_seed.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Runs a draft seed document through the exact parser the Calaf app imports with. Pass the entire JSON document as a string. Returns per-section counts and the precise issue list (path, severity, message): severity "error" means nothing imports; "dropped" means that row or field is discarded and the rest lands. Fix the issues and validate again until clean before delivering the file to the user.
| Name | Required | Description | Default |
|---|---|---|---|
| seed | Yes | The complete seed JSON document — as text, or as the JSON object itself. |
| Name | Required | Description |
|---|---|---|
| valid | Yes | |
| counts | Yes | |
| issues | Yes | |
| hasVocab | Yes | |
| hasPlanner | Yes |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
The annotation readOnlyHint:true indicates no side effects, and the description does not contradict that. It explains the return format (per-section counts and issue list), the meaning of severity levels 'error' and 'dropped', and the iterative validation process, providing full transparency about behavior.
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 concise yet thorough, covering purpose, usage, return details, and the validation workflow in a few well-structured sentences. It does not waste words and focuses on essential information.
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?
Given the tool's simplicity, the description is quite complete. It mentions the return counts and issues, explains severity, and advises iterating until clean. It does not need to detail the seed format itself since that is covered by the sibling get_seed_format, but it could explicitly mention that the tool is read-only; however, the annotation covers this.
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?
The description and schema both clarify the single parameter 'seed' as the complete seed JSON document, either as a string or an object. This fully explains what is expected, with 100% schema description coverage and no ambiguity.
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 clearly states the tool's function: it runs a draft seed document through the exact parser used by the Calaf app, which is a validation operation. It is distinct from the sibling tool get_seed_format, which presumably provides the format, making the purpose unambiguous.
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 explains when to use the tool: after drafting a seed file, validate it and fix issues until clean before delivering. It gives clear instructions to pass the entire JSON document and implies it is the validation step, though it does not explicitly mention the alternative get_seed_format.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Recent tool additions, removals, and schema changes observed during successful MCP inspections. Dates show when Glama detected each change.
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The two tools have clearly distinct purposes: one retrieves the format specification, the other validates a draft document. There is no overlap or ambiguity in their roles.
Both tool names follow a consistent 'verb_noun' pattern: get_seed_format and validate_seed. The naming is clear, descriptive, and predictable.
With only 2 tools, the server is minimal but they cover the essential workflow of format retrieval and validation. It feels slightly thin but is reasonable for a focused purpose.
The server covers the core workflow (authoring and validating), but there is no tool for creating or handling the seed itself — likely the app handles that externally. Notable gaps could be generation or transformation tools, but the provided surface is coherent.
