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kohasummons

ChainGPT MCP

by kohasummons

Server Quality Checklist

67%
Profile completionA complete profile improves this server's visibility in search results.
  • Latest release: v1.0.0

  • Disambiguation5/5

    Each tool serves a distinct purpose: news retrieval, chat history, and chat invocation. There is no overlap or ambiguity in their functionality.

    Naming Consistency5/5

    All tools follow a consistent 'chaingpt_verb_noun' pattern (e.g., chaingpt_get_ai_crypto_news, chaingpt_invoke_chat), making it easy to predict tool names.

    Tool Count4/5

    With 3 tools, the set is minimal but covers the core features of the server (news, chat, chat history). While more tools could be added, the count is reasonable for the focused scope.

    Completeness4/5

    The tool surface includes the primary operations: reading news, sending chat messages, and retrieving history. Minor gaps exist (e.g., no tool for managing sessions or filtering news by source), but the essentials are covered.

  • Average 3.6/5 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
    • Last stable release on
    • No critical vulnerability alerts
    • No high-severity vulnerability alerts
    • No code scanning findings
    • CI status not available
  • This repository is licensed under MIT License.

  • 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

  • Behavior3/5

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

    With no annotations, the description carries the full burden. It adds a cost warning and outlines capabilities, but does not disclose response format, error handling, or performance constraints. The behavioral disclosure is partial.

    Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

    Conciseness3/5

    Is the description appropriately sized, front-loaded, and free of redundancy?

    The description is verbose with redundant sections (e.g., capabilities list and repeated purpose statement). It could be more concise and front-loaded with essential information.

    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 no annotations, no output schema, and zero schema description coverage, the description provides some context about ChainGPT's domain but misses details on response handling, error cases, and parameter usage. The contradictions reduce completeness.

    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 description adds context for chatHistory (default "off") but contradicts the schema, which requires it. It also introduces sdkUniqueId not present in the schema. Schema coverage is 0%, so description should compensate but instead introduces inconsistencies.

    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 it invokes a chat with ChainGPT AI and gets a response based on a question, specifying it's a Web3 AI assistant. However, it does not explicitly differentiate from sibling tools chaingpt_get_ai_crypto_news and chaingpt_get_chat_history, missing an opportunity to guide selection.

    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 includes a cost warning, implying when use is appropriate, but lacks explicit guidance on when to use this tool versus its siblings. It does not state alternatives or when not to use it.

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

  • Behavior3/5

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

    With no annotations, the description must disclose behavioral traits. It mentions default behavior tied to API key and the selectable chat blob ID, but fails to mention read-only nature, authentication requirements, rate limits, or what happens when limit is exceeded. It describes retrieval but does not confirm non-destructive behavior.

    Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

    Conciseness3/5

    Is the description appropriately sized, front-loaded, and free of redundancy?

    The description includes a docstring with Args and Returns sections, making it structured but not overly concise. It could be shortened by removing redundant repetition of schema details while keeping key behaviors.

    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?

    The tool has 5 required parameters and no output schema. The description does not explain the return format, pagination behavior, error conditions, or any constraints like maximum limit. The return value is vaguely described as 'The chat history' without structure, leaving the agent underinformed.

    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 coverage is 100%, so the schema documents all 5 parameters fully. The description repeats parameter information in a structured Args list, adding little beyond the schema. It provides default values and order information, but no unique semantic insight.

    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 'Get the chat history for a given chat blob id' using a specific verb and resource. It distinguishes from siblings like 'chaingpt_get_ai_crypto_news' and 'chaingpt_invoke_chat' by focusing on chat history retrieval.

    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 provides some context on when to use the sdkUniqueId (to filter by chat blob) versus default (all chat blobs), but does not explicitly compare to alternative tools or state when not to use this tool. The guidance is implicit but not comprehensive.

    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?

    Discloses API call cost (1 credit per 10 records), sources scanned (CoinDesk, etc.), and default behavior (10 articles). Does not describe error handling or rate limits beyond cost, but provides sufficient transparency for safe invocation.

    Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

    Conciseness3/5

    Is the description appropriately sized, front-loaded, and free of redundancy?

    The description is verbose and contains redundant phrases (e.g., 'source, filter, deduplicate, and summarize' repeated). While well-structured with sections, it would benefit from trimming unnecessary repetition.

    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?

    Covers all aspects: purpose, parameter details, cost, default behavior, and return value (articles with summaries and links). No output schema, but description adequately explains outputs. Complete for a news retrieval tool.

    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?

    All 8 parameters are explained in detail beyond schema descriptions, with examples for categoryId, subCategoryId, tokenId, fetchAfter, and sortBy. The description adds meaningful context that aids correct parameter usage.

    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 it retrieves AI-related crypto and web3 articles, using specific verbs like 'get' and 'fetch', and distinguishes from sibling tools (chat history and invoke chat) by focusing on news retrieval.

    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 explicit guidance on calling without parameters and how to use limit/offset for pagination. Includes cost warning, but lacks explicit when-not-to-use scenarios or comparisons with alternatives beyond mentioning it's built for crypto news.

    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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  • Evaluate tool definition quality.

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