jsonify
jsonifyCoerces messy text (key=value lines, CSV-ish, sentences) into clean JSON. [price: $0.001/call USDC via x402]
Input Schema
TableJSON Schema
| Name | Required | Description | Default |
|---|---|---|---|
| text | Yes | Input text |
ThirdMade Shelf
TDQS
B3.3/5.0
Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are present, so the description carries the full burden of behavioral disclosure. It reveals a transformation behavior and pricing, but does not disclose output format details, failure modes for unparseable input, or any side-effect/safety characteristics. Some burden was met, but significant gaps remain.
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 stating the core transformation and input examples, followed by a terse pricing note. There is no filler, repetition, or unnecessary detail; every element earns its place.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness4/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a simple one-parameter tool, the description covers the purpose, input formats, and outcome ('clean JSON'), and mentions cost. It doesn't describe the exact JSON shape or error behavior, but given the tool's low complexity and the presence of the main informational need, it is largely complete.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters4/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The schema only describes the text parameter generically as 'Input text', while the description adds meaningful semantics by enumerating accepted formats (key=value lines, CSV-ish, sentences). Even with 100% schema coverage, this elaboration improves an agent's ability to format input correctly.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose4/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly identifies a specific action ('Coerces') and resource ('messy text...into clean JSON'), and lists concrete input formats (key=value lines, CSV-ish, sentences). It doesn't explicitly differentiate from sibling json_guard, but the transformation-focused wording makes the purpose reasonably distinct.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description implies the tool is for converting messy text into JSON, but it provides no explicit when-to-use guidance, no exclusion cases, and no mention of alternatives such as json_guard for validation. An agent must infer the appropriate context from the examples alone.
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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TDQS
B3.2/5.0
Disambiguation3/5
Most tools have distinct purposes, but several clusters overlap: domain_facts, page_meta, and scrape all return page title information, and search_verify, hallucination_check, and sweep all target claim validation. The descriptions usually clarify the use case, but the boundaries are not always obvious.
Naming Consistency3/5
All names use lowercase snake_case, so there is a baseline consistency, but the pattern is mixed: bare verbs like scrape, summarize, and sweep sit alongside noun+noun forms like domain_facts and noun+verb forms like entity_find. The names are readable but do not form a predictable verb_noun API convention.
Tool Count3/5
At 26 tools, this is heavy and above the typical well-scoped 3-15 range, though the server is explicitly positioned as a broad shelf of paid utilities. Many tools are small one-purpose endpoints, so the count feels more like a catalog than a focused suite, but it is not an extreme mismatch.
Completeness4/5
The shelf covers the major advertised areas: web page analysis, research verification, text guards and NLP, blockchain reads, and image generation. There are some gaps such as no web search and no transaction sending, but agents can typically work around them or pair this with another server.