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archoor

PainSpotter MCP Server

by archoor
OverviewSchemaRelated ServersScoreDiscussions
Python
Hybrid

Server Quality Checklist

75%
Profile completionA complete profile improves this server's visibility in search results.

  • Latest release: v1.1.0

  • Disambiguation5/5

    Each tool has a clearly distinct purpose: two for blog posts (list and get by slug) and two for opportunities (query with filters and get by ID). No overlap in functionality.

    Naming Consistency5/5

    All tool names follow a consistent verb_noun snake_case pattern: get_blog_post, get_opportunity, list_blog_posts, query_opportunities. No mixing of styles.

    Tool Count4/5

    With 4 tools, the set is small but well-scoped for a server providing read-only access to blog posts and opportunities. It covers the core operations without being overly minimal.

    Completeness5/5

    The tool surface is complete for a read-only information retrieval server: users can list and get individual blog posts, and query opportunities with filters and retrieve full details. No obvious gaps given the domain.

  • Average 4.5/5 across 4 of 4 tools scored.

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

    • No community issues in the last 6 months
    • 6 commits in the last 12 weeks
    • Last stable release on
    • No critical vulnerability alerts
    • No high-severity vulnerability alerts
    • No code scanning findings
    • CI is passing

  • This repository is licensed under MIT License.

  • This repository includes a README.md file.

  • No tool usage detected in the last 30 days. Usage tracking helps demonstrate server value.

    Tip: use the "Try in Browser" feature on the server page to seed initial usage.

  • This repository includes a glama.json configuration file.

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    {
  "$schema": "https://glama.ai/mcp/schemas/server.json",
  "maintainers": [
    "your-github-username"
  ]
}

    Then . Browse examples.

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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

  • Behavior4/5

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

    Annotations already declare readOnlyHint=true, destructiveHint=false, and idempotentHint=true. The description adds context beyond annotations: 'Free; no API key required' and 'recent published' filter. It does not contradict annotations.

    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 concise: two sentences and a bullet. It front-loads the purpose in the first sentence. No wasted words.

    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 simplicity (one optional parameter, annotations covering safety, and an output schema), the description covers purpose, parameter semantics, and free access. No missing information.

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

    Parameters5/5

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

    The input schema has 0% description coverage (no description for limit), but the description explicitly explains the limit parameter: 'Number of posts to return, 1-30. Default 10.' This adds complete semantic meaning.

    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 action: 'List recent published PainSpotter blog posts'. It specifies the resource (blog posts) and scope (recent published), distinguishing it from siblings like get_blog_post (single post) and get_opportunity/query_opportunities (opportunities).

    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?

    The description mentions it's free and requires no API key, implying low barrier to use. However, it does not explicitly state when to use this tool versus alternative tools like get_blog_post, nor does it provide exclusions or prerequisites.

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

  • Behavior4/5

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

    Annotations (readOnlyHint, idempotent) already indicate safe read. Description adds value by detailing returned fields (description, score breakdown, etc.), enhancing transparency.

    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?

    Two concise sentences plus args line, front-loaded with purpose. No unnecessary words, efficient.

    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 output schema exists, description covers return fields. Sibling context and annotations provide sufficient completeness for a simple detail retrieval tool.

    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?

    Only parameter 'opportunity_id' is explained as coming from query_opportunities, adding context beyond the integer type in schema. With 0% schema coverage, description compensates well.

    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 retrieves full detail of a specific opportunity, listing included fields. It distinguishes from sibling 'query_opportunities', which is for listing.

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

    Usage Guidelines4/5

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

    Indicates that opportunity_id comes from query_opportunities results, implying usage after listing. Lacks explicit when-not-to-use but sibling context provides guidance.

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

  • Behavior4/5

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

    Annotations already declare readOnlyHint, destructiveHint, idempotentHint, openWorldHint. The description adds that it is free and requires no API key, plus the content nature (AI synthesis). Adds value beyond annotations.

    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?

    Two short paragraphs: first line states purpose, second adds usage context, then Args section. Front-loaded, no wasted words, every sentence earns its place.

    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?

    Tool is simple with one param. Description covers purpose, param guidance, cost/auth, and content nature. Output schema exists (not shown) so return value explanation is unnecessary. Complete for a read operation.

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

    Parameters5/5

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

    Schema describes slug as type string with no description. The description adds 'Article slug, taken from list_blog_posts results', giving provenance and usage guidance. With 0% schema coverage, this fully compensates.

    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 explicitly states 'Get the full Markdown body of one published blog post by its slug', which is a specific verb+resource pair. It clearly distinguishes from siblings like list_blog_posts and get_opportunity.

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

    Usage Guidelines4/5

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

    Provides clear context: 'Free; no API key required' and notes the content is an original AI synthesis. It implies the slug comes from list_blog_posts, guiding workflow. No explicit when-not-to-use, but sufficient for the task.

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

  • Behavior4/5

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

    Annotations already indicate readOnlyHint and idempotentHint; the description adds that results are sorted by overall score descending, which is useful behavioral context beyond annotations.

    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 concise with a clear summary followed by parameter details in a bullet-like format, no wasted sentences.

    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 5 parameters, all optional with defaults, and an output schema present, the description fully explains inputs, sorting, and defaults, making it complete for an agent.

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

    Parameters5/5

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

    With 0% schema coverage, the description fully compensates by providing examples, defaults, and clear explanations for each parameter (e.g., 'e.g. sleep tracker' for query).

    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 explicitly states 'Search PainSpotter opportunities' with specific filter criteria, clearly distinguishing it from the sibling 'get_opportunity' which retrieves a single item.

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

    Usage Guidelines4/5

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

    The description implies usage for searching/filtering vs. get_opportunity for single retrieval, but no explicit 'when not to use' or alternative conditions are given.

    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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