graphql_operations_list
List GraphQL query/mutation/subscription names and type defs. When: List GraphQL ops/types from a document (best-effort).
Input Schema
| Name | Required | Description | Default |
|---|---|---|---|
| text | Yes |
List GraphQL query/mutation/subscription names and type defs. When: List GraphQL ops/types from a document (best-effort).
| Name | Required | Description | Default |
|---|---|---|---|
| text | Yes |
Changes observed during successful MCP inspections. Dates show when Glama detected each change.
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the full burden. It includes the term 'best-effort', implying potential incompleteness, which is a behavioral trait. However, it does not disclose other traits like performance, limits, or 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.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is two sentences, front-loaded with the action and resource. It is concise and to the point. Could be slightly expanded to explain the parameter, but overall efficient.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given one parameter and no output schema, the description is fairly complete. It specifies the input as a document and caveats with 'best-effort'. It does not detail output format or size considerations, but for a list tool, it covers essential aspects.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The input schema provides no description for the 'text' parameter (0% coverage). The description implies that 'text' is a GraphQL document by stating 'List GraphQL ops/types from a document', adding minimal meaning beyond the schema. More explicit guidance would improve.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the verb 'List' and the resource 'GraphQL query/mutation/subscription names and type defs'. It distinguishes itself from sibling tools like graphql_validate or graphql_schema_diff by focusing on listing operations from a document.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description includes a 'When:' clause indicating the context for use (listing GraphQL ops/types from a document). It does not explicitly mention when not to use or provide alternatives, but the context is clear enough for selection.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Add one secure layer between your agents and this server.
Every tool has a clear, distinct purpose with thorough descriptions. Even closely related tools like base64_decode/encode and hash_md5/sha256 are easily differentiated by name and description.
All tools follow a consistent lowercase_underscore naming convention, typically in a <domain>_<action> or <action>_<domain> pattern. There are no jarring deviations or mixed styles.
193 tools is an extreme count, far beyond what any focused server needs. While each tool has utility, the sheer number creates a kitchen-sink effect that overwhelms agents and hinders discoverability.
Within each subdomain (JSON, cron, JWT, etc.), the coverage is exhaustive, covering validation, conversion, parsing, and more. Minor gaps exist (e.g., YAML-to-TOML conversion missing), but overall it is remarkably complete.