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

With no annotations provided, the description carries the full burden, and it explicitly declares 'READ-ONLY: Non-destructive metadata discovery.' It also discloses the two distinct behavioral outcomes based on typeNames and clarifies that the manifest is 'not the full schema,' which helps set expectations. It does not mention potential errors or how headers/endpoint affect behavior, but the core safety and conditional behavior are clearly communicated.

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 well-structured with a clear one-line summary, an explicit READ-ONLY safety cue, and a numbered list that front-loads the two usage modes. Every sentence contributes either to behavioral transparency or invocation guidance, with no wasted words.

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?

For a moderate-complexity tool with zero required parameters and no output schema, the description is largely complete: it covers both invocation modes, the manifest vs. SDL distinction, and the read-only nature. It could be more complete by explicitly routing query execution to query-graphql, but this is a minor gap given the clear 'before executing queries' framing.

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?

Schema description coverage is 100%, so the baseline is 3, but the description adds meaningful behavior beyond the schema by explaining the conditional outcome for typeNames and clarifying that results include 'fields and relations.' This gives an agent a better mental model of what each parameter triggers, even though the schema already documents the parameters themselves.

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 identifies the tool as retrieving GraphQL schema details or a system manifest, with a specific verb and resource. It distinguishes between two modes—full SDL for requested types versus a Federated Manifest overview—and positions it as metadata discovery before executing queries, differentiating it from the sibling query-graphql tool.

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 gives explicit conditional guidance: use typeNames to get full SDL definitions, omit it to get a manifest for navigating the federated graph. It also states to use the manifest 'before executing queries,' providing clear context. However, it does not explicitly name or exclude the sibling tool query-graphql as the alternative for actual query execution.

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

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

With no annotations, the description carries the full burden, and it does so well: it discloses that the tool performs remote operations, that mutations modify persistent state, that authentication is inherited from the environment, and that failures return an errors list. These are the key behavioral facts an agent needs beyond the schema.

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 organized into clear warning, prerequisite, security, and return-value statements with no filler. It is slightly longer than a minimal two-sentence description, but each section contributes a distinct piece of operational knowledge.

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 four parameters and no output schema or annotations, the description covers the essential operating context: when to verify schema, mutation risk, authentication model, and expected return structure. No critical information needed to call the tool correctly is missing.

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?

Schema coverage is 100%, so the baseline is 3; the description adds value by warning that the required query string may contain operations that mutate persistent state, and by noting the return shape. It does not repeat schema descriptions, but provides meaningful semantic context for the query parameter.

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?

States a specific action verb ('Execute') and target ('GraphQL operations ... against the federated system'), distinguishing this as the operation-execution tool while the sibling 'introspect-schema' is referenced only for schema verification. The clarification that it handles both queries and mutations further disambiguates its role.

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?

Explicitly names the sibling tool as a prerequisite step ('Verify schema structure using introspect-schema before executing complex queries'), and warns to execute mutations only when a state change is intended. This gives the agent clear when-to-use and when-not-to-use guidance for mutation operations.

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