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List schemas

list_schemas
Read-only

List the schemas belonging to the authenticated tenant. Schemas are JSON Schemas that datafiles validate against; reference one by schema_id when creating a datafile.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
page_sizeNomaximum number of schemas to return in this page
page_tokenNotoken from a previous response's next_page_token to fetch the next page
slug_prefixNoonly return schemas whose slug starts with this prefix

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
schemasYes
next_page_tokenYes

Schema Changelog

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

  1. First observed

TDQS

A4.3/5.0
Behavior4/5

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

Annotations already declare readOnlyHint=true and destructiveHint=false, so the agent knows this is a safe read operation. The description adds useful behavioral context beyond the annotations: results are scoped to the authenticated tenant, and schemas are JSON Schemas used for datafile validation, which helps the agent understand the returned objects' role.

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 with no redundant phrasing. The core action and scope are front-loaded, and the second sentence adds valuable domain context without padding.

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?

For a read-only list tool with fully documented optional parameters and an output schema, the description covers the essential context. It explains what schemas are, that they are tenant-scoped, and how they are used downstream, so an agent has enough information to invoke it correctly.

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?

Schema description coverage is 100%, so the parameters (page_size, page_token, slug_prefix) are already fully documented in the input schema. The description adds no additional parameter-level detail, but per the baseline for high schema coverage, a score of 3 is appropriate.

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 states a specific verb ('List'), a clear resource ('schemas'), and a scope ('belonging to the authenticated tenant'), which is more precise than the title alone. It also defines what schemas are and how they relate to datafiles, making the tool's role unmistakable among the many list_* siblings.

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 implies when to use this tool by noting schemas are referenced by schema_id when creating a datafile, so an agent can infer this is the lookup step before create_datafile. It does not explicitly name alternatives or exclusions, 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.

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TDQS

A4/5.0
Disambiguation5/5

Every resource family follows the same verb+noun pattern and each tool name uniquely identifies a resource-action pair (create_app vs create_app_version vs update_app vs publish_app). Closest overlaps like analyze_resource vs get_resource_graph and patch_datafile vs update_datafile are explicitly differentiated by their descriptions, so misselection risk is low despite the scale.

Naming Consistency5/5

Names are almost uniformly verb_noun snake_case with a consistent lifecycle vocabulary: create/get/update/delete/list/publish/unpublish/version. Minor outliers like whoami and run_schedule_now are idiomatic and do not break the predictability of the set.

Tool Count1/5

At 93 tools this far exceeds the calibration's 50+ extreme-mismatch case. The count is inflated by repeating create/get/update/delete/version/publish/unpublish across ten resource families; even though each family is systematic, the combined surface is very hard for an agent to navigate and keep in context.

Completeness4/5

Core CRUD/publish/version lifecycles are present for apps, workflows, endpoints, schedules, schemas, datafiles, and api templates, and dependency analysis is well covered. However, secret creation/updating, asset upload, custom-domain deletion, and version-range enumeration for several resource types are absent or left to the external dashboard, so agents hit a few manual dead ends.

Resources