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Get model library

get_library
Read-onlyIdempotent

List the JSONata functions and values a model's library exports — what an expression in this model may call, with signature, arity and originating layer. Call it before writing an expression so you reuse the model's existing vocabulary instead of re-deriving it. Returns null when the model declares no library.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
idYesThe model id.

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
exportsNoOne entry per exported name: {name, kind ('function'|'constant'), signature, arity, origin}.
descriptionNoThe library's prose description, if any.

Schema Changelog

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

  1. First observed

TDQS

A4.5/5.0
Behavior4/5

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

Annotations already declare readOnlyHint=true and idempotentHint=true, so safety is covered. The description adds valuable behavioral context beyond that: it returns null when no library exists and lists what the library contains (signature, arity, layer). This is useful and not redundant with annotations. It doesn't explain format details beyond that, but the output schema likely covers structure. No contradiction.

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, all purposeful: first defines what it does, second gives usage guidance, third states a return edge case. Information is front-loaded with the core purpose before context. Nothing is redundant or fluff.

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 this is a single-parameter read-only tool with a rich output schema and annotations covering safety, the description is complete. It explains what is returned, when to call it, and how to handle a null result. It does not need to explain return format because the output schema is present. No gaps remain.

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 coverage is 100% (the single 'id' parameter is described as 'The model id.'). The description does not add any additional meaning about the parameter beyond what the schema already provides. Per the rubric, when schema coverage is high, a baseline of 3 is appropriate, and the description adds no parameter-specific insights.

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 opens with a specific verb and resource: 'List the JSONata functions and values a model's library exports'. It clarifies what the tool returns (signature, arity, originating layer) and explicitly distinguishes it from sibling tools like get_model_info or get_spec by focusing on reusable expression vocabulary. This is unambiguous and separates it from the 25 siblings.

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?

It gives an explicit call-to-action: 'Call it before writing an expression so you reuse the model's existing vocabulary instead of re-deriving it.' This tells the agent when to use it and the benefit. It also covers the edge case of a model with no library (returns null), which guides handling of a common situation.

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

Most tools have clearly distinct purposes, but get_audit explicitly subsumes get_history and explain, and get_state with paths overlaps get_field, creating minor selection ambiguity. The detailed descriptions help, but an agent could still reach for the wrong getter.

Naming Consistency4/5

Naming is overwhelmingly consistent: snake_case with verb_noun structure and coherent get_/create_/delete_ clusters. Minor deviations like bare verbs (mutate, explain, restore, snapshot) and eval instead of evaluate prevent a perfect score.

Tool Count3/5

27 tools is above the comfortable range and feels heavy, especially with several overlapping audit/state getters that could be consolidated. That said, the domain is broad enough that the count is defensible, so it is heavy but not chaotic.

Completeness4/5

The tool set covers the full model lifecycle well: create, validate, test, mutate, evolve, read, delete, plus snapshot/restore, audit, blobs, views, library, and expression evaluation. Minor gaps like explicit export/import or separate view-management tools are workable around.