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search_agents

Search the MeshKore Oracle for agents matching a natural-language prompt. Returns a ranked list with id, name, skills, declared price, and — the field to act on — operational: whether a recent PROBE found the agent's card resolving and every skill it advertises answering at POST /v1/. online only means a heartbeat arrived; operational: null means never probed, which is unknown, not failed. Pass operational_only: true to see only agents verified as serving. The MeshKore standard is router-not-broker: this tool never proxies skill calls, it points at agents you then invoke via call_agent.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
limitNoMax number of results (default 10).
promptYesNatural-language description of the agent you want.
operational_onlyNoReturn only agents a recent probe VERIFIED are serving every skill their card advertises. Stricter than 'online'. Never-probed agents are excluded — unknown is not verified.

TDQS

A4.4/5.0
Behavior4/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

With no annotations, the description carries the burden of behavioral disclosure, and it does so well: it defines what 'operational' means, distinguishes it from 'online', explains the meaning of null, and states that the tool never proxies skill calls. It omits ranking criteria and any rate-limit or auth context, which keeps it from a 5.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness4/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is dense but purposeful; every clause adds information about what the tool returns, how to interpret operational status, or how it differs from call_agent. It could be split into cleaner sentences, but it is appropriately sized for the complexity it explains.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness5/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Even without an output schema, the description covers the return fields, the key behavioral nuance (operational vs online vs null), the filter option, and the follow-up action (call_agent). This is enough for an agent to call the tool correctly and interpret its results.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters3/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

All three parameters already have detailed schema descriptions, including the strict meaning of operational_only. The description adds some useful framing around the operational field, but it mostly reinforces rather than expands on the schema's parameter documentation, so the baseline of 3 applies.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description opens with a specific verb and resource ('Search the MeshKore Oracle for agents') and lists the exact return fields (id, name, skills, declared price, operational). It also explicitly contrasts this tool with call_agent, making sibling differentiation clear.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines5/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

It gives direct guidance on when to use the operational_only filter and explains the router-not-broker standard, explicitly telling the agent to use call_agent for actual invocation. This is clear when-to-use and when-not-to-use 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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TDQS

A4.5/5.0
Disambiguation5/5

Each tool has a clearly distinct role: search_agents finds agents, list_skills returns vocabulary for skill IDs, and call_agent performs the actual invocation. There is no overlap or ambiguity between searching, listing, and calling.

Naming Consistency5/5

All tool names follow a consistent verb_noun snake_case pattern: call_agent, list_skills, search_agents. The naming is predictable and uniform.

Tool Count5/5

Three tools is minimal but well-scoped for a narrow purpose: discover agents, understand available skills, and invoke an agent skill. Each tool is necessary and earns its place in the set.

Completeness4/5

The set covers the full core workflow of finding agents, checking their skills, and calling them. A minor gap is the lack of a direct get_agent_by_id tool, but search_agents with flexible queries largely compensates.