json_pointer_get
Get value by RFC 6901 JSON Pointer (e.g. /user/id). When: Read one field by RFC 6901 pointer without full walk in the agent.
Input Schema
| Name | Required | Description | Default |
|---|---|---|---|
| data | Yes | ||
| pointer | No |
Get value by RFC 6901 JSON Pointer (e.g. /user/id). When: Read one field by RFC 6901 pointer without full walk in the agent.
| Name | Required | Description | Default |
|---|---|---|---|
| data | Yes | ||
| pointer | No |
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 must fully disclose behavior. It only says 'Get value' and hints at efficiency ('without full walk'), but lacks details on error handling (e.g., invalid pointer), return format, or side effects. For a read operation, the safety profile is implied but not explicit.
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 core purpose. Every sentence adds specific information: what it does and when to use it. No unnecessary words or redundancy.
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 simple tool with 2 parameters and no output schema, the description covers the basic purpose and usage context. However, it omits input format details, error scenarios, and return value structure, which would be necessary for complete agent understanding.
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 description explains the pointer parameter via an example and RFC reference, adding value beyond the schema. However, the data parameter (a JSON string) is not described, and schema coverage is 0%. The description should clarify the expected input format for both parameters.
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 tool gets a value using an RFC 6901 JSON Pointer. It specifies the resource ('value') and the method ('JSON Pointer'), with an example. The sibling set includes similar tools like jsonpath_query, but the description distinguishes by emphasizing pointer-based single field access.
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 that advises using this tool to read one field without a full walk, implying efficiency for single-pointer lookups. It does not explicitly list alternatives or when not to use, but the context is clear enough for an agent to decide.
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.