graphql-introspection-mcp

Server Quality Checklist

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Latest release: v1.1.0
Disambiguation5/5
Each tool has a clearly distinct purpose: schema retrieval, filtering queries, mutations, types, and fetching details for types and fields, plus query execution. No overlap or ambiguity exists.
Naming Consistency5/5
Tool names consistently follow a verb_noun pattern with snake_case: get_graphql_schema, filter_queries, filter_mutations, filter_types, get_type_details, get_field_details, execute_query. Naming is predictable and unified.
Tool Count5/5
With 7 tools, the set is well-scoped for a GraphQL introspection server. Each tool covers a necessary aspect without redundancy, making the count appropriate.
Completeness4/5
The surface covers schema introspection, listing queries/mutations/types, and detailed field/type info, plus query execution. The only notable gap is the absence of a filter for subscriptions, but core workflows are fully covered.
Average 3.4/5 across 7 of 7 tools scored.
See the Tool Scores section below for per-tool breakdowns.
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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?
Annotations declare readOnlyHint=true, which is consistent with the description's 'Filter and list' wording. However, the description adds no behavioral context beyond that, such as whether the tool performs introspection, handles authentication, or what output format to expect.
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, front-loaded sentence that directly states the tool's purpose. No filler or redundant information is present, making it highly concise and appropriately sized.
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?
With no output schema and a sparse description, the tool's return behavior, pagination (if any), and authentication requirements are left unexplained. The description covers only the basic action and resource, which is insufficient for an agent to fully anticipate tool behavior.
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%, so parameter semantics are fully handled by the schema. The description adds no extra meaning beyond indicating that 'filter' relates to the search parameter and 'list' to the overall operation, which is minimal added value.
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 uses a specific verb 'Filter and list' with a clear resource 'GraphQL mutations'. It distinguishes from sibling tools like filter_queries and filter_types by explicitly targeting mutations.
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 such as filter_queries or get_graphql_schema. The name and sibling context imply use for mutations, but the description itself gives no explicit usage context or exclusions.
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?
Annotations declare readOnlyHint=true, so the agent knows it is a safe read. However, the description adds no further behavioral context (e.g., authentication, default endpoint, or response format), failing to go beyond the structured 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 a single, front-loaded sentence with no redundant wording, earning a high score.
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?
Despite having 7 optional parameters and no output schema, the description gives no overview of the default behaviors or return structure. The schema covers parameters, but the description lacks contextual completeness for the tool's operation.
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?
The input schema already provides 100% parameter descriptions, including enums and defaults. The tool description adds no additional parameter meaning, so schema coverage suffices.
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 uses a specific verb ('filter and list') and identifies the resource ('GraphQL types'), clearly differentiating from sibling tools such as filter_queries and filter_mutations.
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 like get_graphql_schema or get_type_details; it only states the function without context or exclusions.
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?
The readOnlyHint annotation already signals a safe read operation, but the description adds no behavioral context beyond that. It does not mention network access, authentication requirements, error behavior, or what 'detailed information' entails, leaving gaps despite the annotation.
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 sentence, front-loaded with the action and resource, containing no unnecessary words. It is perfectly concise and easy to parse.
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?
With no output schema, the description carries the burden of explaining what 'detailed information' means, but it does not. The tool involves six parameters including authentication, yet the description provides no context about return format, usage scenarios, or operational behavior, making it incomplete for a moderately complex tool.
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%, with all six parameters documented in the input schema, including defaults and the enum for operation_type. The description itself contributes no additional parameter semantics, so the baseline of 3 applies.
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 gets detailed information about a specific query or mutation field, using a specific verb and resource. It distinguishes itself from sibling tools like get_type_details (for types) and filter_queries/filter_mutations (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 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. The description lacks explicit when/when-not statements or references to sibling tools, leaving the agent to infer usage purely from the action.
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?
Annotations include readOnlyHint=true, and the description aligns with a read-only operation. It adds no extra behavioral context such as authentication details, rate limits, or response format, but because annotations already cover the safety profile, no contradiction exists.
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, front-loaded sentence with no redundant words. It efficiently communicates 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?
With no output schema, the description fails to clarify what 'detailed information' includes—such as fields, descriptions, or deprecation status. This is a notable gap for an agent deciding whether the tool meets its needs. The presence of sibling tools like get_field_details further emphasizes the need for more specific return-value context.
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?
The input schema has 100% parameter description coverage, so the schema handles parameter semantics. The description adds little beyond mentioning 'specific GraphQL type', which maps to the type_name parameter, but provides no additional details about endpoint or auth parameters.
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 tool retrieves information about a specific GraphQL type, using a specific verb and resource. It distinguishes from siblings like get_graphql_schema (whole schema) and filter_types (filtering), though 'detailed information' remains vague about what exactly is returned.
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. It does not mention excluded use cases or point to sibling tools like get_field_details or filter_types. Sibling names offer context but the description itself gives no selection criteria.
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?
The readOnlyHint annotation indicates a safe read operation, and the description does not contradict it. The description adds the 'complete' scope but offers no additional behavioral context such as authentication requirements or potential response size. Given the annotation covers the safety profile, a 3 is appropriate.
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?
One sentence, perfectly front-loaded, and without any filler. It conveys the core purpose efficiently.
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?
The tool is simple with four optional auth parameters and no output schema. The description states what it returns (the introspection), which is sufficient for the primary use case. However, it could mention authentication for protected endpoints and doesn't address when to use this vs siblings. Given the moderate complexity, a 3 is fair.
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?
The input schema documents all four parameters with descriptions, providing 100% coverage. The description adds nothing about parameters, but the schema adequately explains endpoint, username, password, and bearer_token. Baseline 3.
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 tool retrieves the complete GraphQL schema via introspection, using a specific verb and resource. The 'complete' qualifier distinguishes it from sibling tools that filter or get details, but not explicitly.
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 usage guidance is provided. The description does not indicate when to use this tool versus filter_queries, get_type_details, or other siblings, nor does it mention any prerequisites or exclusions.
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?
Annotations include readOnlyHint=true, which already signals a safe read operation, so the description need not cover safety. The description adds minimal behavioral context beyond 'available' (suggesting introspection), but does not disclose details like whether queries are executed (they are not, per readOnlyHint) or response format. No contradiction with 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 a single sentence with no filler words. It is front-loaded with the key verb and resource, making it immediately understandable. Every word earns its place; ideal conciseness.
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 output schema, the description should clarify what the tool returns (e.g., a list of query names and optionally details). The current statement 'list available GraphQL queries' is somewhat vague about the exact output and how filtering works. It is adequate for a simple listing tool but leaves some gaps in context.
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% for all 6 parameters, so the schema fully documents each parameter. The tool description does not add extra meaning to parameters; it only states the overall purpose. Baseline 3 applies as schema carries the burden.
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 'Filter and list available GraphQL queries' uses a specific verb ('Filter and list') and resource ('available GraphQL queries'), clearly distinguishing this tool from its siblings such as 'filter_mutations' and 'get_graphql_schema'. The scope (queries vs. mutations) is immediately obvious, making it a strong purpose statement.
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 (when you need to list/filter GraphQL queries) but does not explicitly state when to use this tool over alternatives like 'filter_mutations' or 'get_graphql_schema'. It lacks explicit exclusions or alternative tool mentions, so guidance is only implied rather than direct.
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=true, but the description adds a behavioral constraint beyond that: 'Only query operations are allowed.' This clarifies that mutations and subscriptions are not permitted, which is not inferable from readOnlyHint alone. The description also confirms the read-only nature, adding value without contradicting the annotation.
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 sentence of about 15 words, front-loaded with the core action ('Execute a read-only GraphQL query'). Every word contributes to defining the tool's purpose and constraints. There is zero redundancy or filler.
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 7 parameters and no output schema, the description is sufficient for selection and invocation understanding. It specifies the endpoint, operation type, and read-only nature. It doesn't describe return format or authentication details, but those are covered by schema and the general understanding of GraphQL. The sibling context makes the role clear, so a 4 is adequate.
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?
The input schema has 100% description coverage for all 7 parameters, so the baseline is 3. The description does not elaborate on any parameter meanings beyond what the schema provides. It adds no extra semantic context for parameters like 'query' or 'endpoint', so a score of 3 is appropriate.
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's action: 'Execute a read-only GraphQL query against the endpoint.' The verb 'execute' and resource 'GraphQL query' are specific, and the restriction to 'query operations' differentiates it from sibling tools that explore schema (e.g., get_graphql_schema, filter_queries). It is a precise, unambiguous statement.
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 when to use the tool: for executing GraphQL queries, while sibling tools are for schema exploration. The statement 'Only query operations are allowed' provides a usage constraint, but it does not explicitly name alternatives or say 'use this instead of X'. The context signals and sibling names make the differentiation clear, earning a score of 4.
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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