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Glama

get_schema

Discover schema shapes. Pass name for one schema, or omit to list all curated schemas.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
nameNoSchema name; omit to list all

Schema Changelog

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

  1. First observed

TDQS

B3.3/5.0
Behavior3/5

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

With no annotations, the description carries the full burden. It discloses that omitting the name returns all curated schemas and passing a name returns one, which is useful behavioral context. However, it doesn't describe the return format, whether the operation is read-only, error behavior, or what 'curated' means, leaving some gaps for a full understanding.

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?

Two sentences with no filler. The primary purpose is front-loaded, and the optional-name behavior is stated efficiently. Every word earns its place.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness3/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given the simple one-parameter schema and read-only discovery purpose, the description provides enough for an agent to invoke the tool correctly. However, without an output schema and with no annotations, the lack of detail about the returned schema structure or response format leaves some ambiguity for agents expecting specific return semantics.

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 schema already documents the single optional 'name' parameter. The description repeats this behavior without adding new semantic detail, which matches the baseline of 3 for fully-covered parameters.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose4/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description states a specific verb ('Discover') and resource ('schema shapes'), and clarifies the two modes of operation. It doesn't explicitly differentiate from siblings, but the sibling names (extract, normalize, parse, structure, validate) are clearly distinct operations, so an agent can infer that this tool is for retrieving schema definitions.

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

Usage Guidelines2/5

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

The description explains how to use the tool (pass a name or omit), but gives no guidance on when this tool should be preferred over siblings or when alternatives might be more appropriate. There is no mention of exclusions, prerequisites, or related tools.

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

B3.4/5.0
Disambiguation2/5

There is meaningful overlap between parse_invoice, structure_text, and normalize_data, especially since structure_text and normalize_data both target the same schemas and parse_invoice is a more specific version of structure_text for invoices. extract_table, get_schema, and validate_structure are distinct, but the unstructured-text-to-schema tools have unclear boundaries.

Naming Consistency5/5

All tool names follow a consistent verb_noun snake_case pattern: extract_table, get_schema, normalize_data, parse_invoice, structure_text, validate_structure. This makes the tool set predictable and easy to navigate.

Tool Count5/5

Six tools is a well-scoped size for a data structuring and validation server. Each tool addresses a meaningful step in the pipeline without excessive redundancy or unnecessary surface area.

Completeness4/5

The core workflow of extracting, normalizing, and validating structured data is covered, with schema discovery available. The main gap is the lack of a way to create or modify custom schemas, but the curated-schema approach makes this a minor limitation rather than a critical failure.

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