Skip to main content
Glama

call_agent

Invoke a skill on a live MeshKore agent. Reads the agent's A2A card (/.well-known/agent.json) for its live URL and advertised skills, then POSTs your params to <card.url>/v1/ — MeshKore routes, it never proxies the call. Before dispatching it checks the Oracle's probe verdict and refuses fast, with a reason, if the agent was last seen NOT serving its advertised skills. Payment: this tool does NOT pay on your behalf and settles nothing. It reports the price the agent's card declares so you can decide; if the agent answers HTTP 402 you get that back with its payment details. Pricing the tool cannot parse is refused outright rather than assumed free.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
skillYesThe skill id to invoke, exactly as the agent's card advertises it (e.g. 'search-hotels', 'generate-tweet'). See list_skills or the `skills` field of search_agents.
paramsNoSkill-specific parameters; sent verbatim as the JSON request body.
agent_idYesThe MeshKore agent id (from search_agents).

TDQS

A4.6/5.0
Behavior5/5

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

With no annotations provided, the description carries the full behavioral disclosure burden, and it does so richly. It reveals that the tool reads the agent's A2A card, never proxies the call, checks the Oracle's probe verdict before dispatching, refuses with a reason when the agent is not serving, does not pay on behalf of the caller, passes through HTTP 402 details, and refuses unparseable pricing rather than assuming it is free.

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 long but every sentence contributes an operational fact: the core action, routing behavior, pre-dispatch safety check, payment semantics, and error handling. It is front-loaded with the main purpose and proceeds in logical order, with no redundant filler.

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?

Despite no output schema and no annotations, the description covers the key invocation contract: where the call goes, what is checked before dispatch, how pricing is handled, and what happens on 402. It does not explicitly describe the shape of a successful skill response, but the tool's behavior and failure modes are otherwise sufficiently specified for correct invocation.

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?

Schema description coverage is 100%, so the baseline is 3. The description adds value beyond the schema by clarifying that params are sent verbatim as the JSON request body, that skill must match the card's advertised id exactly, that agent_id comes from search_agents, and by giving concrete skill-id examples. This is a meaningful boost over the schema alone.

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: 'Invoke a skill on a live MeshKore agent.' It goes further by explaining the mechanism (reads the A2A card, POSTs to <card.url>/v1/<skill-id>), which clearly distinguishes this tool from the sibling discovery tools list_skills and search_agents.

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

Usage Guidelines4/5

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

The description makes the context clear: you use this after identifying an agent and its skill, and it references list_skills and search_agents for finding the correct skill id. It does not explicitly state 'use this instead of X' or list exclusion cases, but the contrast with the sibling discovery tools is strong enough for an agent to know when this is the invocation step.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Try in Browser

Glama MCP Gateway

Add one secure layer between your agents and this server.

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.