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Glama

Server Details

Deterministic validation for AI-generated artifacts: JSON Schema, OpenAPI response, SQL syntax.

Status
Healthy
Last Tested
Transport
Streamable HTTP
URL
Repository
machinegrade/validate
GitHub Stars
0
Server Listing
machinegrade-validate

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Tool DescriptionsB

Average 3.4/5 across 1 of 1 tools scored.

Server CoherenceA
Disambiguation5/5

Only one tool exists, so there is no possibility of confusion with other tools.

Naming Consistency5/5

With a single tool, naming is trivially consistent.

Tool Count3/5

One tool is on the lower end of appropriate; it's functional but feels minimal for a validation server that could benefit from separate tools for different contract types.

Completeness4/5

The single tool covers major validation types (json_schema, openapi_response, sql), leaving only minor gaps for other potential contract formats.

Available Tools

1 tool
validateBInspect

Validate an artifact against a contract (json_schema | openapi_response | sql) via the machinegrade validate API.

ParametersJSON Schema
NameRequiredDescriptionDefault
typeYesWhich validator to run.
artifactYesThe artifact to validate (object for json_schema/openapi_response, SQL string for sql).
contractNoValidator-specific contract. json_schema: { schema }. openapi_response: { spec, path, method, status }. sql: { dialect }.
Behavior2/5

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 mentions 'via the machinegrade validate API' but does not disclose whether the operation is read-only, synchronous, or has side effects. A validation tool likely does not mutate state, but this is not stated.

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. Every part earns its place: verb, resource, types, and API. No wasted words.

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?

Given no output schema and no annotations, the description should explain return values (e.g., validation result structure) and error cases. It fails to do so, leaving the agent without critical context for interpreting outcomes.

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 coverage is 100%, so the baseline is 3. The description adds a general context of validating artifacts against a contract but does not provide additional meaning beyond the schema's parameter descriptions. The enumeration of types in the description loosely mirrors the schema's enum.

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 'validate' and resource 'artifact against a contract' with explicit type options in parentheses. With no sibling tools, differentiation is not needed, making this a clear statement of purpose.

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?

Although there are no sibling tools requiring when-not guidance, the description provides no usage context such as prerequisites or when to choose each validation type. It is minimally adequate but lacks explicit 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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