structured-extractStructured Extract — Pull structured data out of free text in any JSON shape you describe — classification, field extraction, scoring. Powered by api.meshtool.ai. Input: { text: string, shape: object }. (2 MESH/call, a tool · extraction)
| Name | Required | Description | Default |
|---|---|---|---|
| input | Yes | Payload for structured-extract |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
The description adds behavioral context beyond annotations by disclosing the cost (2 MESH/call) and external dependency (api.meshtool.ai), which aligns with openWorldHint=true. It does not contradict readOnlyHint=false as the operation is stateless extraction; the main side effect is monetary cost, which is mentioned.
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 purpose is front-loaded and clear, but the description contains redundant elements: 'Input: { text: string, shape: object }' duplicates the schema, and trailing metadata like 'a tool · extraction' adds noise. It is still relatively compact, but not every sentence earns its place.
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 description adequately conveys the output behavior (returns structured data in the user-provided shape) even without an output schema. It mentions cost, external API, and typical use cases. It lacks details on error handling or edge cases, but for a simple extraction tool this is sufficient.
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%, so the description does not need to compensate much. It restates the input format (text, shape) but adds minimal extra meaning such as 'any JSON shape you describe' and use-case examples. This provides slight value beyond the schema, but not enough to exceed the baseline.
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's purpose with a specific verb ('Pull') and resource ('structured data out of free text'), and lists representative use cases (classification, field extraction, scoring). It is distinct from sibling tools like 'search' or 'fetch' by emphasizing customizable JSON shape extraction.
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 gives clear context for when to use the tool (extracting structured data from free text into any JSON shape). It does not explicitly name alternatives or exclusions, but the use case is evident and the 'any JSON shape' phrasing highlights its flexibility. This exceeds a vague statement but stops short of explicit guidance.
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.
Several tools have overlapping purposes: search and mesh_discover both enumerate the catalog, while biz-analyze, task-analysis, and task-orchestrate all produce structured plans from a described situation. This will cause agents to misselect between them despite otherwise distinct tools.
Naming is inconsistent: mesh_* tools use snake_case, most capability tools use hyphenated lowercase names, and a few (fetch, search) are bare verbs. There is no single verb-object or noun-verb pattern that holds across the set.
28 tools is on the heavy side, but the marketplace concept justifies including many callable capabilities. However, the mix of platform tools and unrelated utilities makes the surface feel cluttered and hard to navigate.
The core marketplace lifecycle is well covered: signup, discover, fetch, publish, delegate, refer, follow, subscribe, and balance. Minor gaps exist (no unpublish or edit for listings), but most agent workflows can proceed without dead ends.