Get all values from a JSON object.
| Name | Required | Description | Default |
|---|---|---|---|
| json_string | Yes | JSON object string |
| Name | Required | Description | Default |
|---|---|---|---|
| type | No | ||
| count | No | ||
| error | No | ||
| valid | No | ||
| values | No | ||
| value_types | 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 carries full responsibility for behavioral disclosure. It lacks details on error handling (e.g., invalid JSON, non-object input), recursion depth for nested objects, or output format. The minimal statement does not provide sufficient transparency beyond the basic function.
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 a single, front-loaded sentence with no extraneous words. It conveys the core purpose efficiently, earning its place without waste.
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?
The tool is simple with one parameter and an existing output schema, reducing the need for extensive description. While the description is minimal, it provides the essential context for what the tool returns. Given the complexity, it is largely sufficient, though a note on unsupported input types would enhance completeness.
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% with one parameter described as 'JSON object string.' The description adds 'JSON object' context, which slightly clarifies the expected input format. However, since coverage is high, the baseline is 3, and the marginal addition is minor.
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 'Get all values from a JSON object,' specifying a distinct verb ('get') and resource ('values from a JSON object'). It differentiates from siblings like 'get_keys' (which retrieves keys) and 'get_json_path' (which retrieves a specific path), establishing a unique purpose.
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 implies using this tool to extract all values from a JSON object but does not explicitly state when to use it versus alternatives or mention any prerequisites (e.g., valid JSON input). There is no guidance on when not to use it, leaving some ambiguity for the agent.
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.
Many tools have overlapping purposes, such as multiple random generators (random_integer, random_number), duplicate hashing functions (hash_md5, md5_checksum), and near-identical tools (compare, compare_2, compare_decimals). The sheer number of tools and lack of clear boundaries make it difficult for an agent to differentiate.
Naming is highly inconsistent. There are duplicate tools with different names (camel_case vs to_camel_case, slug vs slugify), arbitrary suffixes like '_2', and mixing of patterns (e.g., generate_password vs password_entropy). No clear convention is followed.
With 572 tools, the server is massively overpopulated for any coherent purpose. It includes trivial endpoints (true_endpoint, null, hello_world) and numerous duplicates, far exceeding a well-scoped utility set.
While the server covers many domains (math, strings, dates, colors, etc.), the presence of duplicate and trivial tools indicates a lack of thoughtful curation. There are gaps in basic operations (e.g., no dedicated file or network tools), and many tools are redundant.
