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MisarBlog MCP Server

MisarBlog MCP Server

by Misar-AI
OverviewSchemaSourceRelated ServersDiscussionsAdmin
TypeScript
Hybrid

Research topic

research_topic
Read-only

Researches a topic to generate AI-powered insights, sources, and a content outline, helping you prepare before writing an article.

Instructions

Research a topic and get AI-generated insights, sources, and a content outline. Useful before writing an article.

Tool call template

Replace each placeholder with a value matching the input schema. Only required parameters are included; add optional parameters as needed.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {
      "query": "<query>"
    },
    "name": "research_topic"
  }
}

Input Schema

NameRequiredDescriptionDefault
queryYesResearch topic or question. Be specific for best results.

Schema Changelog

Changes observed during successful MCP inspections.

  1. Changed1 schema field changedv5.2.0
    • removedInput schema / additionalProperties
      Removed value: -false
  2. Changed1 schema field changedv5.1.3
    • changedInput schema / properties / query / description
      Previous value: -"The topic or question to research, 5-500 characters. Specific beats broad: 'how small SaaS teams price annual plans' returns more than 'pricing'."New value: +"Research topic or question. Be specific for best results."
  3. Changed1 schema field changedv5.1.0
    • changedInput schema / properties / query / description
      Previous value: -"Research topic or question. Be specific for best results."New value: +"The topic or question to research, 5-500 characters. Specific beats broad: 'how small SaaS teams price annual plans' returns more than 'pricing'."
  4. First observedv2.0.0

TDQS

A3.8/5.0
Behavior4/5

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

The annotations already establish that this is a non-destructive, read-only, open-world operation. The description adds meaningful behavioral context by disclosing the output shape: AI-generated insights, sources, and a content outline, which is valuable because no output schema is provided.

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?

The description is two sentences, front-loaded with the core action and followed by a brief usage context. Every phrase contributes either capability or usage information, with no redundant wording.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness4/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

This is a simple one-parameter, read-only tool with a fully documented input schema and annotations covering safety. The description explains the return content even without an output schema, leaving only minor gaps such as freshness, source quality, or expected latency.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters3/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 100%, and the single query parameter is fully documented in the schema as a research topic or question with advice to be specific. The tool description only restates 'Research a topic' and adds no parameter-specific syntax or constraints beyond the schema, so the baseline score of 3 is appropriate.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose4/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description states a specific verb and resource: 'Research a topic' and lists concrete outputs: AI-generated insights, sources, and a content outline. It is clear what the tool does, though it does not explicitly differentiate itself from sibling research or generation tools such as search_articles or suggest_titles.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines3/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

It gives an implied usage context with 'Useful before writing an article,' which helps an agent understand when this tool may be appropriate. However, it does not state when not to use it or name alternatives, so the guidance remains implicit rather than explicit.

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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