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kukapay

TheGraph MCP Server

by kukapay
OverviewSchemaRelated ServersScoreDiscussions
Python
Remote

Server Quality Checklist

67%
Profile completionA complete profile improves this server's visibility in search results.

  • Latest release: v1.0.0

  • Disambiguation5/5

    The two tools have clearly distinct purposes: getSubgraphSchema retrieves schema information via introspection, while querySubgraph executes actual GraphQL queries. There is no overlap in functionality, making it easy for an agent to select the correct tool based on the need for metadata versus data retrieval.

    Naming Consistency5/5

    Both tools follow a consistent verb_noun pattern (getSubgraphSchema, querySubgraph) using camelCase, which is predictable and readable. The naming convention is uniform across all tools, with no deviations or mixed styles.

    Tool Count2/5

    With only 2 tools, the server feels thin for interacting with The Graph protocol, which typically involves more operations like listing subgraphs, managing deployments, or handling subscriptions. This limited set may restrict agents from performing comprehensive tasks in the domain.

    Completeness2/5

    The tool surface is severely incomplete for a The Graph server, lacking essential operations such as listing available subgraphs, creating or updating subgraphs, and handling mutations or subscriptions. Agents will face dead ends when trying to perform basic lifecycle management or discover subgraphs beyond a known ID.

  • Average 4.1/5 across 2 of 2 tools scored.

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

    • 0 of 1 community issues answered or closed 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

  • 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 provided, the description carries full burden. It discloses the return format options (JSON or GraphQL text) and mentions error messages, but lacks details about authentication requirements, rate limits, or what happens with invalid subgraph IDs.

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

    Conciseness4/5

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

    The description is well-structured with a clear purpose statement followed by Args and Returns sections. It's appropriately sized, though the 'Args' and 'Returns' labels could be more integrated with the flow rather than section headers.

    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?

    For a tool with 2 parameters, no annotations, and no output schema, the description provides good coverage of purpose, parameters, and return behavior. It could be more complete by addressing authentication or error scenarios, but covers the essentials adequately.

    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 description coverage, the description fully compensates by clearly explaining both parameters: 'subgraphId' specifies which subgraph to query, and 'asText' controls the return format (text vs JSON). This adds essential meaning beyond the bare schema.

    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 specific action ('Fetch the schema') and resource ('specified subgraph using GraphQL introspection'), distinguishing it from the sibling 'querySubgraph' which presumably executes queries rather than fetching schema metadata.

    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 implies usage for obtaining schema information via introspection, but does not explicitly state when to use this tool versus the sibling 'querySubgraph' or provide any exclusions or prerequisites for usage.

    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 provided, the description carries full burden for behavioral disclosure. It states the tool executes queries and returns results or errors, which covers basic functionality. However, it doesn't mention authentication requirements, rate limits, timeout behavior, or whether queries are read-only or can mutate data. The description adds some value but lacks comprehensive behavioral context.

    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 perfectly structured with a clear purpose statement followed by well-organized sections for Args and Returns. Every sentence earns its place, with no redundant information. The formatting with clear headings makes it easy to parse while remaining extremely concise.

    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 complexity of GraphQL query execution and the lack of both annotations and output schema, the description provides adequate but minimal information. It covers the basic purpose and parameters but doesn't explain the format of query results beyond 'JSON format', nor does it address error handling, authentication, or other operational considerations that would be helpful for an AI agent.

    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?

    With 0% schema description coverage, the description must compensate for the lack of parameter documentation in the schema. It clearly explains that 'subgraphId' identifies which subgraph to query and 'query' is the GraphQL query string to execute, providing essential semantic meaning beyond the bare schema. However, it doesn't specify format requirements or constraints for these parameters.

    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 specific action ('Execute a GraphQL query') and resource ('against a specified subgraph'), distinguishing it from the sibling tool 'getSubgraphSchema' which presumably retrieves schema information rather than executing queries. The verb+resource combination is precise and unambiguous.

    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 context by specifying it's for executing GraphQL queries against subgraphs, but doesn't explicitly state when to use this tool versus alternatives like 'getSubgraphSchema' or other query methods. It provides clear context but lacks explicit comparison or exclusion guidance.

    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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