biz-analyzeBusiness Analyze — Analyze any business situation and get a structured recommendation back — the original api.meshtool.ai capability, now settled in MESH. Input: { context: string }. (2 MESH/call, a tool · reasoning)
| Name | Required | Description | Default |
|---|---|---|---|
| input | Yes | Payload for biz-analyze |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare openWorldHint=true, readOnlyHint=false, and destructiveHint=false. The description adds the cost per call (2 MESH/call) and the fact it is a 'reasoning' tool, but does not disclose additional behavioral traits like potential external data fetching or response format. No contradiction with annotations.
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 concise (one sentence) and front-loaded with the core purpose. However, it includes brand-related fluff ('the original api.meshtool.ai capability, now settled in MESH') that does not help an AI agent and slightly detracts from 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?
The description gives a clear purpose and input format, but lacks details on return structure (no output schema), usage examples, and differentiation from sibling tools. For a tool with annotations and a simple schema, it is minimally complete but leaves gaps.
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 description coverage is 100%, so the input schema already documents context, goal, and constraints. The description only mentions '{ context: string }' and adds no extra meaning beyond the schema. Baseline 3 is appropriate.
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 uses a specific verb ('Analyze') and identifies the resource ('any business situation') and the outcome ('get a structured recommendation back'). However, it does not distinguish this tool from sibling tools like task-analysis or structured-extract, which also involve analysis.
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 explicit guidance on when to use this tool versus alternatives. It only says 'Analyze any business situation,' which is broad and does not mention exclusions or other tools. There is no mention of use cases that would favor a sibling.
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.