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Glama

List Schemas

list_schemas
Read-only

List all schemas of an API specification. Optionally include usage counts showing how many endpoints reference each schema. Set scope to 'project' to look across every spec in the project instead — that returns the groups of structurally identical schemas (candidates for the shared library) plus counts, not the full list, and specId is then ignored. Requires project context.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
scopeNoWhat to look at: 'spec' (default) or 'project'spec
specIdYesPublic ID (GUID) of the API specification
versionIdNoOptional version ID (GUID) to filter schemas by a specific version
includeUsageCountsNoInclude usage counts per schema (default false, slightly slower)

Schema Changelog

Changes observed during successful MCP inspections. Dates show when Glama detected each change.

  1. First observed

TDQS

A4.2/5.0
Behavior4/5

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

The readOnlyHint annotation already covers safety, so the description does not need to repeat that. It adds a behavioral note that includeUsageCounts is 'slightly slower' and clarifies that project scope returns groups of structurally identical schemas, which goes beyond the annotation.

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 two sentences, well-structured, and directly addresses the core behavior and the key variation (project scope). Every clause adds relevant information without redundancy or fluff.

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?

Given the lack of an output schema, the description covers the essential operational context: what the tool lists, the optional usage counts, the special project-scope behavior, and the requirement for project context. It does not detail the output structure, but that is reasonable since no output schema is provided.

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 input schema already describes all four parameters, so the baseline is high. The description enriches this by explaining the interaction between scope and specId (specId is ignored in project scope) and by noting the performance implication of includeUsageCounts, adding value beyond 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 clearly states the tool's function with a specific verb ('List') and resource ('all schemas of an API specification'). It also differentiates the project scope behavior, helping distinguish it from related siblings like get_schema or list_shared_schemas.

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

Usage Guidelines3/5

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

The description gives some usage context, such as setting scope to 'project' for cross-spec analysis and noting that specId is ignored in that mode. However, it does not explicitly mention when to use this tool versus alternative tools or name alternatives, leaving some ambiguity about selection.

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

A3.9/5.0
Disambiguation4/5

The tools are mostly distinct with clear descriptions. Some pairs like get_header_policies vs get_resolved_headers or get_environment_verification vs get_monitoring_sync_status could be slightly confusing, but the descriptions clarify scope and purpose.

Naming Consistency5/5

All tools follow a consistent snake_case verb_noun pattern (get_, list_, create_, update_, manage_, etc.). Even the few bare verbs like 'search' and 'set_context' are consistent with the naming scheme.

Tool Count1/5

With 165 tools, the server is extremely heavy. This far exceeds the 'too many' threshold of 25+, making it difficult for an agent to navigate and select the right tool efficiently.

Completeness5/5

The tool surface covers a very broad API lifecycle domain: specs, environments, test cases, monitors, mock servers, security, governance, documentation, and team management. Read and write operations are present across most areas, with no obvious missing core functionality.

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