toml_validate
Validate TOML text; return parsed object or error.
Input Schema
| Name | Required | Description | Default |
|---|---|---|---|
| text | Yes |
Validate TOML text; return parsed object or error.
| 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?
With no annotations, the description carries full burden. It clearly states the core behavior: validating TOML text and returning either a parsed object (success) or an error (failure). This adequately conveys that the tool is non-destructive and returns a standard result, though it could mention that the operation is stateless and has no 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 extremely concise at 9 words, yet covers the essential purpose and outcome. It has no unnecessary information, but for a tool with a single parameter, slightly more detail (e.g., what constitutes a valid TOML file) would not hurt conciseness.
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 the simplicity of the tool (one required parameter, no output schema), the description covers the main functionality. However, it lacks information about the error format or the structure of the parsed object, which might be needed for integration. It is minimally viable but leaves some ambiguity.
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 only parameter, 'text', is described only by its name. The description does not add any additional context such as expected TOML format version, character encoding, or constraints. With 0% schema description coverage, the description fails to compensate for the lack of detail in the input schema.
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 'Validate' and resource 'TOML text', and specifies the outcome ('return parsed object or error'). It effectively distinguishes from sibling tools like toml_schema_validate (schema validation) and toml_to_json (conversion), making the tool's purpose unambiguous.
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 provides no guidance on when to use this tool versus alternatives such as json_validate, yaml_validate, or toml_schema_validate. It does not mention that this is for syntax validation only, nor does it direct users to other tools for schema or format conversion.
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.