Misar.Blog MCP Server

Generate SEO titles from keywords

generate_title_seo

Read-only

Generate 5 search-optimised article titles from a TOPIC OR KEYWORD, each with a keyword-strategy hint. Aims at high-volume, low-competition long-tail phrases and at AI answer engines (ChatGPT, Perplexity, Claude) as well as Google.

Pick between the two title tools by what you have in hand: use this one when you have a topic or keywords and the article may not be written yet. Use suggest_titles when the draft already exists and you want titles drawn from its actual text. Passing context here does not make them equivalent — this one still optimises for the keywords you supply.

Nothing is saved and no article is created or retitled; use update_article to apply a title. Requires an API key and consumes AI credits per call. Generative, so repeated calls return different titles.

Input Schema

TableJSON Schema

Name	Required	Description	Default
`prompt`	Yes	Topic or target keywords, 3-500 characters. Include niche, audience and any long-tail phrase you want to rank for, e.g. 'best AI writing tools for beginner bloggers 2025'.
`context`	No	Optional draft text (plain or Markdown, up to 8000 chars) so the titles match what the article actually says. Titles still follow `prompt` for keywords.

TDQS

A4.7/5.0

Behavior4/5

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

Annotations already cover readOnlyHint=true, destructiveHint=false, and idempotentHint=false. Description adds that nothing is saved or created, requires API key and consumes credits, and is generative (non-deterministic). This adds behavioral context beyond annotations, though the annotation already signals read-only and non-destructive. The API key and credit consumption details are helpful extras.

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?

Description is a single paragraph, front-loaded with purpose, then usage guidance, then side effects and permissions. Every sentence adds useful information without redundancy. It is concise and well-structured.

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?

Given the tool's complexity (generative, two parameters, no output schema), the description covers purpose, usage alternatives, side effects, and parameter roles. The absence of an output schema is not a gap since the description specifies it returns titles with hints. It also mentions API key requirement and non-determinism, making it 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% and the schema descriptions are informative (e.g., what to include in prompt, context limits). However, the description adds context about how the parameters interact (context does not override prompt) and clarifies the optimization goal for AI answer engines. This adds value beyond the schema descriptions.

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?

Description clearly states what it does: 'Generate 5 search-optimised article titles from a TOPIC OR KEYWORD'. The verb 'generate' and resource 'article titles' are specific likely, and it explicitly distinguishes from suggest_titles by noting the difference in input (topic/keyword vs existing draft).

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?

Explicitly instructs when to use this tool: when you have a topic or keywords and the article may not be written yet, and when not to (use suggest_titles for existing drafts). Also clarifies that passing context does not make them equivalent, giving clear disambiguation.

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.

Resources

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