recommend_capabilityReturn the best matching real SqueezeOS capabilities for a natural-language need, including live endpoint/payment metadata and next actions.
| Name | Required | Description | Default |
|---|---|---|---|
| limit | No | ||
| query | Yes |
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?
Annotations already declare this safe/read-only/idempotent, and the description does not contradict that. It adds useful context ('live endpoint/payment metadata', 'next actions') but does not disclose possible side effects, rate limits, or authorization requirements beyond what the annotations capture.
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?
A single front-loaded sentence states the core purpose and the valuable output extras without repetition or filler.
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?
With no output schema, the description gives a useful high-level picture of the return payload (capabilities, live metadata, next actions) and both parameters are covered by schema/description together. It stops short of describing output formatting or how matching works, but that is not required for invocation.
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?
With 0% schema description coverage, the description adds crucial meaning to the required query parameter by framing it as a natural-language need. It does not elaborate on the optional limit parameter, but its meaning is readily inferable from its name, default, and min/max constraints.
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 ('Return') and resource ('real SqueezeOS capabilities') and specifies the input ('natural-language need') and output extras ('live endpoint/payment metadata and next actions'). 'Best matching' distinguishes it from sibling discovery/inspection tools even without naming them.
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?
It clearly states the context for use: when an agent has a natural-language need and wants the best matching capability. It does not explicitly exclude alternatives such as discover_capabilities or describe when to prefer those, so it stops short of full routing guidance.
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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Most tools have clear boundaries: discover searches, inspect examines one, compare contrasts, recommend interprets natural language, and call executes. Some ambiguity exists between capability_manifest and discover_capabilities for inventory listing, and recommend_capability could be confused with discover_capabilities, but descriptions generally prevent misselection.
All names are lowercase snake_case, and the core capability operations follow a verb_noun pattern. The noun-led capability_manifest, system_status, and x402_* resources deviate from that pattern, and capability names mix singular and plural, but the naming remains readable and internally consistent per subdomain.
Ten tools is well within the ideal range and each tool maps to a distinct part of the capability discovery, comparison, payment, and execution workflow. There are no obviously redundant or throwaway tools, so the count feels appropriately scoped.
The set covers the full read-only market-intelligence lifecycle: inventory, search, inspect, compare, recommend, call, payment readiness, contract lookup, and settlement evidence. Operator CRUD is explicitly outside scope, so there are no meaningful dead ends for an agent using this surface.