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tussanakorn

MCP Blog API

by tussanakorn
OverviewSchemaRelated ServersScoreDiscussions
Python

Server Quality Checklist

50%
Profile completionA complete profile improves this server's visibility in search results.

  • Latest release: v1.0.0

  • Disambiguation5/5

    Each tool has a clearly distinct purpose: add_blog creates new blogs, get_blogs retrieves all blogs, and search_blogs finds blogs by title. There is no overlap in functionality, making tool selection straightforward for an agent.

    Naming Consistency5/5

    All tool names follow a consistent verb_noun pattern with snake_case: add_blog, get_blogs, and search_blogs. This predictability enhances readability and usability across the tool set.

    Tool Count3/5

    With only 3 tools, the set feels thin for a blog API that typically requires CRUD operations. While the tools cover creation and retrieval, the absence of update and delete tools limits the server's scope, making it borderline appropriate.

    Completeness2/5

    The tool set is significantly incomplete for a blog API domain. It lacks update_blog and delete_blog tools, preventing full lifecycle management. This gap will likely cause agent failures when attempting to modify or remove blogs.

  • Average 2.9/5 across 3 of 3 tools scored.

    See the Tool Scores section below for per-tool breakdowns.

    • No community issues in the last 6 months
    • 0 commits in the last 12 weeks
    • No stable releases found
    • No critical vulnerability alerts
    • No high-severity vulnerability alerts
    • No code scanning findings
    • CI status not available

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  • This repository includes a README.md file.

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How is the quality score calculated?

The overall quality score combines two components: Tool Definition Quality (70%) and Server Coherence (30%).

Tool Definition Quality measures how well each tool describes itself to AI agents. Every tool is scored 1–5 across six dimensions: Purpose Clarity (25%), Usage Guidelines (20%), Behavioral Transparency (20%), Parameter Semantics (15%), Conciseness & Structure (10%), and Contextual Completeness (10%). The server-level definition quality score is calculated as 60% mean TDQS + 40% minimum TDQS, so a single poorly described tool pulls the score down.

Server Coherence evaluates how well the tools work together as a set, scoring four dimensions equally: Disambiguation (can agents tell tools apart?), Naming Consistency, Tool Count Appropriateness, and Completeness (are there gaps in the tool surface?).

Tiers are derived from the overall score: A (≥3.5), B (≥3.0), C (≥2.0), D (≥1.0), F (<1.0). B and above is considered passing.

Tool Scores

  • Behavior2/5

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

    No annotations are provided, so the description carries full burden. It states 'Add a new blog' which implies a write/mutation operation, but doesn't disclose behavioral traits such as authentication needs, rate limits, idempotency, or what happens on success/failure. This is inadequate for a mutation tool with zero annotation coverage.

    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 a single, efficient sentence with zero waste. It's appropriately sized and front-loaded, stating the core purpose without unnecessary elaboration.

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

    Completeness2/5

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

    Given a mutation tool with no annotations, 0% schema coverage, no output schema, and 2 required parameters, the description is incomplete. It lacks parameter explanations, behavioral context, and usage guidelines, making it insufficient for effective tool invocation.

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

    Parameters1/5

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

    Schema description coverage is 0%, so the schema provides no parameter descriptions. The description doesn't mention parameters at all, failing to add any meaning beyond the input schema. For a tool with 2 required parameters, this leaves semantics completely undocumented.

    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 clearly states the action ('Add a new blog') and target resource ('to the mock API'), providing a specific verb+resource combination. However, it doesn't differentiate from sibling tools like 'get_blogs' or 'search_blogs', which would require mentioning this is for creation versus retrieval/search operations.

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

    Usage Guidelines2/5

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

    The description provides no guidance on when to use this tool versus alternatives. It doesn't mention prerequisites, context for creation, or compare to sibling tools like 'get_blogs' or 'search_blogs', leaving the agent without explicit usage instructions.

    Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

  • Behavior2/5

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

    With no annotations provided, the description carries the full burden of behavioral disclosure. It mentions the search action and API source but omits critical details such as whether this is a read-only operation, expected response format, error handling, or any rate limits. The description is minimal and lacks depth for a tool with behavioral implications.

    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 a single, efficient sentence with zero waste. It is appropriately sized and front-loaded, directly stating the tool's purpose without unnecessary elaboration.

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

    Completeness2/5

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

    Given the lack of annotations and output schema, the description is incomplete. It covers the basic action but misses essential context like return values, error cases, and behavioral traits. For a search tool with no structured support, this leaves significant gaps in understanding.

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

    Parameters2/5

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

    The schema description coverage is 0%, so the description must compensate. It only vaguely implies that the 'query' parameter is for title searches, without detailing syntax, format, or constraints. This adds minimal meaning beyond the bare schema, failing to adequately document the single required parameter.

    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 clearly states the action ('Search for blogs') and the resource ('blogs'), specifying the search criterion ('by title') and the data source ('using the mock API'). It distinguishes from sibling 'get_blogs' by indicating a search rather than a general retrieval, but doesn't explicitly differentiate from 'add_blog' beyond the verb difference.

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

    Usage Guidelines2/5

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

    No explicit guidance is provided on when to use this tool versus alternatives like 'get_blogs' or 'add_blog'. The description implies usage for title-based searches but lacks context on prerequisites, exclusions, or comparative scenarios with siblings.

    Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

  • Behavior2/5

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

    With no annotations provided, the description carries the full burden of behavioral disclosure. It mentions fetching all blogs but does not specify aspects like rate limits, authentication needs, pagination, or error handling. This leaves significant gaps in understanding how the tool behaves in practice.

    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 a single, efficient sentence that directly states the tool's function without unnecessary words. It is front-loaded and wastes no space, making it easy to parse quickly.

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

    Completeness3/5

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

    Given the tool's simplicity (0 parameters, no output schema, no annotations), the description is adequate but minimal. It covers the basic purpose but lacks details on behavior, usage context, or output format, which could be helpful for an agent despite the low complexity.

    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?

    The tool has 0 parameters, and the input schema has 100% description coverage (though empty). The description does not need to add parameter details, so it meets the baseline expectation for a parameterless tool by not introducing confusion or redundancy.

    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 clearly states the action ('Fetch') and resource ('all blogs from the mock API'), making the purpose evident. However, it does not explicitly differentiate from the sibling 'search_blogs', which might also retrieve blogs but with filtering capabilities, leaving some ambiguity about when to choose one over the other.

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

    Usage Guidelines2/5

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

    No guidance is provided on when to use this tool versus alternatives like 'search_blogs' or 'add_blog'. The description implies it fetches all blogs without filtering, but this is not stated explicitly, leaving the agent to infer usage context without clear directives.

    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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