mcp-graphql-bridge

Overview Schema Related Servers Score Discussions

Server Quality Checklist

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

Latest release: v2.2.0
Disambiguation4/5
The query__* tools are clearly separated by resource and singular/plural variant, and get_type_details serves a distinct introspection role. execute_graphql overlaps with the specific query tools by design, but its fallback purpose is clearly stated, so misselection is unlikely.
Naming Consistency4/5
The six query tools follow a consistent query__<resource> pattern, and the singular/plural distinction is predictable. The generic tools get_type_details and execute_graphql use a different verb_noun style, which is a minor deviation but not confusing.
Tool Count5/5
Eight tools is a lean, focused set for a GraphQL bridge: six common resource queries plus two generic utilities for schema inspection and arbitrary operations. Each tool has a clear purpose and the count feels appropriate.
Completeness5/5
The generic execute_graphql tool ensures any query or mutation can be run, covering coverage gaps beyond the named resources. get_type_details supports the workflow of discovering types and fields, so the surface has no obvious dead ends.
Average 2.4/5 across 8 of 8 tools scored. Lowest: 1.6/5.
See the Tool Scores section below for per-tool breakdowns.
- No community issues in the last 6 months
- 15 commits in the last 12 weeks
- No stable releases found
- No critical vulnerability alerts
- No high-severity vulnerability alerts
- No code scanning findings
- CI is failing
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.
Add a glama.json file to provide metadata about your server.
If you are the author, simply .
If the server belongs to an organization, first add glama.json to the root of your repository:
```
{
  "$schema": "https://glama.ai/mcp/schemas/server.json",
  "maintainers": [
    "your-github-username"
  ]
}
```
Then . Browse examples.
Add related servers to improve discoverability.

How to sync the server with GitHub?

Servers are automatically synced at least once per day, but you can also sync manually at any time to instantly update the server profile.

To manually sync the server, click the "Sync Server" button in the MCP server admin interface.

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

Behavior1/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
There are no annotations, so the description carries full responsibility for behavioral disclosure. It discloses nothing about authentication requirements, error behavior, rate limits, mutation safety, or what the query returns. The bare query prefix gives the agent no meaningful behavioral understanding.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness2/5
Is the description appropriately sized, front-loaded, and free of redundancy?
"[QUERY] language" is extremely short, but this is under-specification rather than effective conciseness. It contains no useful content and earns no structural credit for front-loading meaningful information.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness1/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
This tool has four parameters, no annotations, no output schema, and a GraphQL-like interface with sibling operations. The description is completely inadequate for an agent to know how to call it correctly, what the response looks like, or how it relates to query__languages and execute_graphql.
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 the schema already documents all four parameters, including the required code and the optional __fields, bearer_token, and custom_headers. The description adds no extra parameter meaning, but the baseline of 3 applies because the structured schema carries the parameter documentation burden adequately.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose1/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description "[QUERY] language" merely restates the tool name and operation prefix without stating what the tool actually does, what a language is in this context, or how it differs from siblings like query__languages. It provides no specific verb and resource description beyond the name itself, making it essentially a tautology.
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 query__languages, query__countries, or get_type_details, nor does it mention any exclusions or alternatives. An agent is left to infer usage from the name alone.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior1/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description must disclose behavior, but it reveals nothing beyond the word 'query'. It does not mention authentication requirements, read-only nature, error behavior, pagination, or any side effects.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness2/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is extremely short, but this is under-specification rather than effective conciseness. It provides no useful information beyond the tool name and does not earn its place as a functional description.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness1/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
The tool has 4 parameters, nested objects, no output schema, and no annotations, yet the description offers no context about return shape, how to use the required code parameter, or how this query behaves. An agent cannot reliably select or invoke this tool based on the description alone.
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 the schema already documents all parameters including code, __fields, bearer_token, and custom_headers. The description adds no parameter-level meaning, but the baseline of 3 applies because the schema carries the burden.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose2/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description '[QUERY] continent' merely restates the tool name and does not explain what querying a continent returns or how it differs from siblings like query__continents, query__countries, or query__country. It names a resource but provides no distinguishing information.
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?
There is no guidance on when to use this tool versus alternatives such as query__continents or query__countries. The description gives no context about selection criteria, prerequisites, or situations where a different tool would be appropriate.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior1/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are present, so the description carries full responsibility for behavioral disclosure. It only says 'query countries' and mentions no auth requirements, read-only nature, pagination, filtering behavior, or return format. This is effectively a missing behavioral description.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness2/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is extremely short, but this is under-specification rather than effective conciseness. It provides no front-loaded useful structure because it contains almost no substantive content beyond the tool name.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness1/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
With no annotations, no output schema, and multiple sibling tools, the description is far too thin to help an agent call this tool correctly. It fails to explain what countries data is returned, how filtering works, what fields are available, or how this differs from query__country.
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 the baseline is 3 even though the description itself says nothing about parameters. The schema already documents filter, __fields, bearer_token, and custom_headers, so the description does not need to add much here.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose2/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description is "[QUERY] countries", which simply restates the tool name query__countries. It indicates the operation and resource but adds no information beyond the name itself, so it falls into tautology rather than a meaningful 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 Guidelines2/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
There is no guidance about when to use this tool versus siblings like query__country or query__continents. The plural 'countries' weakly implies listing multiple countries, but the description offers no explicit context, conditions, 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?
With no annotations, the description bears the full burden of behavioral disclosure. '[QUERY]' at least implies a read operation, but nothing is said about side effects, authentication expectations, filtering behavior, pagination, or return shape.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness2/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is extremely short, but this is under-specification rather than useful conciseness. A two-word label cannot convey the behavior or selection criteria an agent needs.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness1/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
This tool has four parameters, no output schema, no annotations, and many sibling query tools, yet the description provides no operational context. An agent cannot reliably know what filter formats are expected, whether __fields is required, or how this differs from the singular continent query.
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 documents all four parameters with 100% coverage. The description adds no additional meaning to filter, __fields, bearer_token, or custom_headers, so baseline 3 is appropriate.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose2/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description is simply '[QUERY] continents', which essentially restates the tool name. It offers no meaningful explanation of what the tool returns or how it differs from the sibling query__continent.
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?
There is no guidance on when to use this tool versus query__continent, query__countries, or execute_graphql. The agent must infer the intended usage entirely from the name, with no explicit 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?
With no annotations, the description carries the full burden of behavioral disclosure, but "[QUERY]" only suggests a read-style operation. It does not disclose authentication needs, required argument behavior, return shape, or any other behavioral traits an agent would need to invoke it confidently.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness2/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is short but under-specified rather than appropriately concise. It is a single unhelpful phrase with no structure, no context, and no explanatory value for an agent.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness1/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
This tool has four parameters, one required field, nested objects, and ambiguous sibling relationships, yet the description provides none of the context needed to select or call it correctly. It is effectively a stub.
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%, and the schema already documents `code`, `__fields`, `bearer_token`, and `custom_headers`. The description adds no parameter meaning, but per the high-coverage baseline, it does not need to compensate.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose2/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description "[QUERY] country" is a tautology that restates the tool name without explaining what querying a country returns, how it is identified, or how it differs from query__countries or execute_graphql. It identifies a resource and a generic operation, but stops short of a usable 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 Guidelines2/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
No guidance is provided about when to use this tool versus siblings like query__countries or execute_graphql. There is no mention of prerequisites, alternative tools, or conditions such as needing a specific country code.
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, the description carries the full burden of behavioral disclosure. '[QUERY]' weakly implies a read-only operation, but no traits such as pagination, filtering behavior, authentication requirements, or response format are disclosed.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness2/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is extremely short, containing no wasted words, but it is under-specified rather than appropriately concise. It lacks the structure needed to convey meaningful usage.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness1/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
The description provides almost no context for a tool with optional parameters, no output schema, and no annotations. It fails to explain what languages are returned, how the filter works, or how this tool relates to sibling query__language and execute_graphql.
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 each parameter documented including __fields, bearer_token, custom_headers, and filter type. The description adds no extra parameter meaning, but the schema already covers this dimension, so baseline 3 is appropriate.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose3/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description '[QUERY] languages' identifies a query operation on a language resource, which is more than a pure restatement but still vague. It gives no detail on what is returned or how it differs from the sibling query__language beyond plurality.
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?
There is no guidance on when to use this tool versus alternatives. Sibling query__language and query__languages are listed nearby, but the description makes no attempt to distinguish them or explain selection criteria.
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?
No annotations are provided, so the description bears full responsibility for behavior disclosure. It notes that mutations can be executed, implying side effects, but does not warn about destructiveness, authentication requirements, rate limits, or error behavior. For a tool that can run arbitrary mutations, this is a significant omission.
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 sentences with no filler: the action is front-loaded, and the usage routing condition follows directly. Every clause earns its place.
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 high-complexity tool that accepts raw GraphQL, the description should clarify the response format and caution about side effects. It only covers purpose and usage, leaving invocation semantics like response handling and safety incomplete.
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?
Input schema covers 100% of parameters with descriptions, so baseline 3 applies. The tool description itself adds no parameter meaning beyond the schema, but the schema already documents each field's purpose adequately.
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?
States a specific verb ('Execute') and resource ('any GraphQL query or mutation against the API'), and distinguishes itself from siblings by positioning itself as the generic fallback when no specific tool exists. The behavior is immediately clear 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?
Explicitly says 'Use this when no specific tool exists for your operation,' which is a clear positive condition and implies the exclusion when a specific tool is available. It doesn't name the specific sibling tools, but the condition is sufficient for routing an agent to look for more targeted alternatives.
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 carries the full burden of behavioral disclosure. It correctly implies a read-only introspection operation, but it does not describe the output shape, behavior for unknown types, or any side effects.
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 one concise sentence with no redundant words. The main action and purpose are front-loaded immediately.
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 single-parameter read-only introspection helper, the description provides enough context to call it correctly. Minor gaps like exact output structure or error behavior do not seriously hinder use.
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 single typeName parameter is already fully documented in the schema, including examples. The description adds no additional parameter-level detail, only overall purpose.
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 names a specific resource ('fields of a specific GraphQL type') and a concrete use case ('to know what to put in __fields'). This makes it easy to distinguish from sibling tools like execute_graphql.
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?
It clearly indicates when to use the tool: when the agent needs to know the fields to supply to __fields. It does not explicitly name sibling alternatives or exclusions, so it stops short of full routing guidance.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

GitHub Badge

Glama performs regular codebase and documentation scans to:

Confirm that the MCP server is working as expected.
Confirm that there are no obvious security issues.
Evaluate tool definition quality.

Our badge communicates server capabilities, safety, and installation instructions.

Card Badge

Copy to your README.md:

[![mcp-graphql-core MCP server](https://glama.ai/mcp/servers/k-edge/mcp-graphql-core/badges/card.svg)](https://glama.ai/mcp/servers/k-edge/mcp-graphql-core)

Score Badge

Copy to your README.md:

[![mcp-graphql-core MCP server](https://glama.ai/mcp/servers/k-edge/mcp-graphql-core/badges/score.svg)](https://glama.ai/mcp/servers/k-edge/mcp-graphql-core)

Latest Blog Posts

Who's Calling? MCP Hosts Are an Identity Blind Spot (And the Spec Knows It)
By Om-Shree-0709 on July 25, 2026.
mcp
Agent Identity
OAuth 2.1
Your AI Chatbot Just Exposed Your CEO's Salary to an Intern
By Om-Shree-0709 on July 2, 2026.
Agent Identity
MCP Security
OAuth Delegation
Why MCP Servers Need Execution Sandboxing (And Why Your Current Stack Isn't Enough)
By Om-Shree-0709 on June 30, 2026.
Agentic Ai
Prompt Injection
WebAssembly

MCP directory API

We provide all the information about MCP servers via our MCP API.

curl -X GET 'https://glama.ai/api/mcp/v1/servers/k-edge/mcp-graphql-core'

If you have feedback or need assistance with the MCP directory API, please join our Discord server