Misar.Blog MCP Server

Generate a cover image

generate_cover_image

Generate an image from a text prompt with AI, upload it to the Misar.Blog CDN, and return its public URL for use as cover_image_url when publishing.

Use it when no artwork exists yet; use upload_image for a file the user already has. Each call generates a NEW image and costs generation credits against the account's plan — it is not idempotent, so re-running to 'try again' bills again. Generation takes noticeably longer than other tools.

Requires an API key. The resulting URL is public and cannot be deleted through this server. Results vary between runs for the same prompt.

Input Schema

TableJSON Schema

Name	Required	Description	Default
`size`	No	Output dimensions: '1792x1024' landscape (the default, best for article covers), '1024x1024' square, '1024x1792' portrait.	1792x1024
`prompt`	Yes	What the image should show, in plain language, up to 1000 characters. Describe subject and style; avoid asking for text in the image.

TDQS

A4.9/5.0

Behavior5/5

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

The description adds significant behavioral context beyond annotations: generation credits, non-idempotence, longer execution time, API key requirement, public URL irreversibility, and output variability. No contradiction with annotations (idempotentHint=false aligns with 'not idempotent').

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?

Every sentence delivers critical information: purpose, when to use, costs, timing, API key, deletion limitations, and variability. No redundancy or filler, well-organized with key facts front-loaded.

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 a tool with no output schema, the description explains the return value (public URL for cover_image_url) and covers all relevant operational aspects (credits, API key, side effects). Complete enough for an agent to invoke 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 100%, so baseline is 3. The description adds value by advising 'Describe subject and style; avoid asking for text in the image' and clarifies size usage (best for article covers), which enriches prompt and size semantics.

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 clearly states the tool's function: 'Generate an image from a text prompt with AI, upload it to the Misar.Blog CDN, and return its public URL for use as cover_image_url when publishing.' This specific verb+resource combination distinguishes it from sibling tools like upload_image.

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?

Explicit guidance: 'Use it when no artwork exists yet; use upload_image for a file the user already has.' It also warns about non-idempotent behavior and costs, providing clear context for when to choose this tool.

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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TDQS

A4.1/5.0

Disambiguation4/5

The tools mostly map to clearly distinct resources and actions, with good behavioral separation between create_draft, publish_article, and update_article, and between the two title-generating tools. Some naming choices are still slightly misleading, such as get_series returning the full collection rather than a single series, and generate_cover_image references a non-existent upload_image tool.

Naming Consistency4/5

The vast majority of tools follow a clean lowercase verb_noun pattern, including create_*, get_*, list_*, and add_* names. Notable exceptions are upgrade, a bare verb that also mixes read and mutate behaviors, and get_series, which functions more like a list than a get.

Tool Count3/5

23 tools is in the heavy range for an MCP server, exceeding the ideal 3-15 span. That said, the tools do span legitimate blogging concerns such as articles, reactions, series, newsletters, analytics, and AI assistance, so the count feels broad rather than padded.

Completeness2/5

The surface covers article creation, reading, updating, and publishing, but there is no delete or unpublish for articles/drafts, no way to remove an article from a series, and no update/delete for series. The dangling reference to upload_image in generate_cover_image also suggests a missing tool, and agents will hit dead ends trying to undo publication or remove content.

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