Tessryx

Create datafile

create_datafile

Create a new datafile holding a JSON object. A datafile MUST be bound to a schema (schema_id) — the schema validates the JSON and generates the human editor; author or pick one with the schema tools first. Property order in json is preserved verbatim. Publish it with publish_datafile to serve it from the CDN.

Input Schema

TableJSON Schema

Name	Required	Description
`json`	Yes	The JSON object this datafile holds. Property order is preserved verbatim.
`slug`	Yes	URL-safe slug, optionally hierarchical.
`schema_id`	Yes	Id of the schema this datafile's JSON is validated against (required). The datafile's content must conform to it, and the schema generates the human editing form.
`description`	No	What this datafile holds.
`display_name`	No	Human-readable name.
`use_latest_schema`	No	Validate against the latest (draft) schema version instead of the published one.

Output Schema

TableJSON Schema

Name	Required	Description	Default
`json`	No
`slug`	Yes
`schema_id`	No
`datafile_id`	Yes
`description`	Yes
`display_name`	Yes
`content_sha256`	No
`last_published_path`	No

Schema Changelog

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

First observedSep 7, 2026

TDQS

A4.4/5.0

Behavior4/5

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

The description highlights key behavioral aspects: the datafile must be bound to a schema, schema validation applies, property order is preserved, and publishing is a separate step. Annotations indicate non-destructive behavior (destructiveHint false), which aligns with the 'create' action. It does not mention auth or rate limits, but these are not critical for a create operation.

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 concise, consisting of two sentences that are direct and free of fluff. It front-loads the core purpose and then provides essential caveats (schema requirement, property order, publishing step) without unnecessary elaboration.

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?

For a creation tool, the description covers the necessary workflow context: the prerequisite (schema), the creation action, and the follow-up (publish). It does not detail the output or error cases, but these are typically not required in a tool description. Overall, it provides sufficient context for an agent to use the tool correctly.

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 schema descriptions already cover all parameters (100% coverage), and the tool description adds context about the relationship between schema_id and json (schema validates the JSON, generates the human editor). It also reinforces the preservation of property order. While redundant with schema descriptions in some places, it adds meaningful workflow context, so it is above average.

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 purpose: 'Create a new datafile holding a JSON object.' It uses a specific verb (create) and resource (datafile), and distinguishes it from sibling tools like create_schema or create_dynamic_endpoint by emphasizing the datafile-specific requirement of being bound to a schema.

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 provides useful usage guidance: it specifies the prerequisite of having a schema ('author or pick one with the schema tools first') and the next step after creation ('Publish it with publish_datafile to serve it from the CDN'). It does not explicitly contrast with update_datafile, but the creation vs. update distinction is clear from the tool name and context.

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.

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