Skip to main content
Glama

Core: List providers

metrecept_list_providers

Lists configured upstream vendors and their readiness, BYOK header names, and billing models so you can debug failed chat completions or verify a vendor is available. Reports key presence only.

Instructions

Show which upstream vendors the gateway has configured and ready (mock, openai, anthropic and each OpenAI-compatible vendor), with BYOK header name and billing model. Use it to debug a failed metrecept_create_chat_completion or check a vendor is ready. Not for picking a model id: use metrecept_list_models. Reports key presence only; keys are never validated upstream or returned. Read-only, not billed, live. Requires a Metrecept API key (METRECEPT_API_KEY env on stdio, or Authorization: Bearer on remote HTTP); results are scoped to that key's tenant only.

Input Schema

NameRequiredDescriptionDefault

No arguments

Output Schema

NameRequiredDescriptionDefault
resultYes

Schema Changelog

Changes observed during successful MCP inspections.

  1. Addedv0.1.4

TDQS

A4.9/5.0
Behavior5/5

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

No annotations exist, so the description carries the full burden and does so: it discloses that results are read-only, not billed, and live, that key presence is reported without upstream validation and keys are never returned, and that results are tenant-scoped to the calling API key. Auth mechanics for both stdio and remote HTTP transports are spelled out.

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?

Purpose and vendor coverage are front-loaded, followed by usage, the sibling exclusion, the key-handling caveat, and the auth/scoping note. Every sentence contributes a distinct decision-relevant fact, and telegraphic fragments like "Read-only, not billed, live." keep it compact.

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?

An output schema exists, so return values need not be explained; the description instead covers the things structured fields cannot — auth requirements, tenant scoping, and the fact that keys are not validated. Nothing an agent needs to invoke this correctly is missing.

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?

The tool takes zero parameters, so per the baseline this sits at 4. The description adds useful framing (no filtering is available; output is scoped by the API key's tenant), which is the only semantic surface available for a zero-arg tool.

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?

States a specific verb and resource ("Show which upstream vendors the gateway has configured and ready") and enumerates the concrete vendor set including the OpenAI-compatible case. It also explicitly distinguishes itself from the nearest sibling by declaring it is not for model selection and naming metrecept_list_models.

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?

Gives two concrete use cases (debug a failed metrecept_create_chat_completion, verify a vendor is ready) and an explicit exclusion routing the agent to metrecept_list_models for model ids. Both the when and the when-not are present, so no inference is required.

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