Alter Onboarding

list_operations

List the API operations a provider exposes, live from Alter's provider-spec catalog (e.g. "what can I call on google?"). Returns operation ids + methods/paths, plus the spec's source and freshness. Omit kind to auto-detect the provider family; when the id exists in both oauth and managed you'll be asked to pass kind. Machine-readable rows ride in structuredContent (see this tool's outputSchema) — read those rather than parsing the prose.

Input Schema

TableJSON Schema

Name	Required	Description
`kind`	No	Provider family: oauth (user-authorized) or managed (API-key).
`limit`	No	Max operations to return (backend default 100, max 500).
`offset`	No	Zero-based offset for paging through large operation lists.
`search`	No	Case-insensitive filter over operation ids/paths/summaries.
`provider_id`	Yes	Provider id, e.g. "google" or "github".

Output Schema

TableJSON Schema

Name	Required	Description
`total`	Yes	Total operations matching the query (before limit/offset).
`offset`	Yes	Zero-based offset of the first row.
`operations`	Yes	This page of operations, in the catalog's serving order.
`provider_id`	Yes	Resolved provider id the rows belong to.
`spec_version`	Yes	Alter's ingested spec version these rows came from.
`provider_kind`	Yes	Resolved provider family: oauth or managed.

TDQS

A4.7/5.0

Behavior5/5

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

With no annotations, the description carries the full burden, and it delivers: it discloses live sourcing, return contents (ids, methods/paths, source, freshness), the auto-detection ambiguity, and instructs agents to read structuredContent instead of prose. This goes well beyond a generic listing description.

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?

Three sentences, front-loaded with purpose, no filler. Each sentence adds distinct value: what the tool lists, what it returns, how to handle `kind`, and how to read results. Excellent structure for an agent-facing definition.

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?

Given the rich output schema and fully documented parameters, the description covers everything an agent needs: purpose, key behavior, disambiguation, and the preferred machine-readable output path. No critical operational detail 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?

Schema coverage is 100%, so baseline is 3. The description adds value by explaining `kind` semantics (auto-detect vs. explicit oauth/managed) and giving a concrete provider example. The remaining parameters are already fully documented in the schema.

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 uses a specific verb ('List') and resource ('API operations a provider exposes'), with a concrete example ('what can I call on google?') that makes the tool's purpose immediately obvious. It also distinguishes this from sibling tools like list_providers or get_operation_schema without needing to name them.

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?

Gives clear contextual guidance: omit `kind` for auto-detection and expect to be asked for it when both oauth and managed exist. It doesn't explicitly state when to prefer an alternative like get_operation_schema, but the context is clear enough for correct selection.

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

All Alter Onboarding tools Inspect

Try in Browser

RayrunBuild, deploy, and operate MCP servers without becoming an infrastructure team.ray.runAd

Glama MCP Gateway

Add one secure layer between your agents and this server.

TDQS

A4.1/5.0

Disambiguation4/5

Most tools target clearly distinct concerns: docs, provider operations, SDK wiring, policies, troubleshooting, and verification. However, get_started with no arguments overlaps with list_phases, and fetch_doc/list_skills both provide guidance content, creating minor ambiguity.

Naming Consistency4/5

The set mostly follows a verb_noun pattern: fetch_doc, list_operations, list_providers, verify_integration, troubleshoot. A few names like policy_language, sdk_integration, and sdk_pattern are noun-led, which is a small consistency break but still predictable and readable.

Tool Count5/5

Thirteen tools is well within the ideal range for a focused onboarding server. Each tool contributes to a different part of the onboarding workflow, and none feel redundant or like filler.

Completeness5/5

The server covers the full onboarding journey: docs, lifecycle phases, step-by-step flows, provider API discovery, schema retrieval, SDK integration, SDK patterns, policy grammar, troubleshooting, and verification. There are no obvious dead ends or major missing operations for its stated purpose.

Resources

Need Help?