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openfda-mcp-server

by io.github.cyanheads

Openfda Dataframe Describe

openfda_dataframe_describe
Read-onlyIdempotent

List the tables and column schemas on a DataCanvas staged by an openFDA search tool. Call before openfda_dataframe_query to discover the exact table name, column names, and DuckDB types needed for valid SQL. row_count is the full staged result set, not the inline preview count. Columns typed JSON hold nested openFDA objects/arrays — query them with DuckDB json functions.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
canvas_idYesCanvas ID from an openFDA search tool response (present when the search ran with stage=true).

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
errorNoPresent when the call failed. Absent on success.
tablesNoAll tables and views available on this canvas.
canvas_idNoCanvas ID that was described — pass to openfda_dataframe_query.

TDQS

A4.7/5.0
Behavior5/5

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

Annotations already declare readOnlyHint=true and idempotentHint=true. The description adds value beyond these by explaining row_count's full-scope meaning and warning that JSON columns hold nested objects needing DuckDB json functions. These are behavioral details not covered by annotations or schema, significantly aiding correct usage.

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 with zero redundancy. The core purpose and usage timing are front-loaded, followed by two essential caveats. Every sentence earns its place, making the description both compact and information-dense.

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?

With an output schema present (so return values need no explanation) and annotations covering read-only/idempotent behavior, the description supplies the remaining critical context: call order, row_count semantics, and JSON column handling. Nothing essential is missing for an agent to invoke this tool 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?

The input schema fully describes canvas_id (coverage 100%), including its source and the stage=true condition. The description adds no new parameter-specific meaning beyond reinforcing that the canvas comes from an openFDA search tool, which the schema already states. Per the baseline for high coverage, a 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 clear verb ('list') and specific resource ('tables and column schemas on a DataCanvas staged by an openFDA search tool'). It differentiates itself from siblings by positioning as a discovery step for the subsequent query tool, making its purpose unambiguous.

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?

Explicitly instructs to 'Call before openfda_dataframe_query' to discover exact names and types, giving direct when-to-use guidance. It also clarifies row_count semantics ('full staged result set, not inline preview count'), preventing a common misuse. This fully addresses usage 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

Each tool targets a distinct FDA endpoint or analytic function. Search tools are separated by domain (adverse events, animal events, recalls, tobacco, etc.), and utility tools (count_values, describe_fields, dataframe_describe/query, drug_profile) serve unique purposes without overlap. An agent can clearly identify which tool to call for a given task.

Naming Consistency5/5

All tools follow a consistent openfda_<action>_<object> pattern in snake_case. Search tools use search_<domain>, while utility tools use clear verbs like get, lookup, count, describe, and query. The only slight deviation is drug_profile (a noun phrase), but it remains intuitive and fits the naming style.

Tool Count5/5

With 14 tools, the server is well-scoped for its purpose of providing access to multiple openFDA endpoints. Each tool covers a distinct resource or analytic capability, and none feel redundant. The count sits comfortably within the ideal range for a domain-specific server.

Completeness5/5

The server covers the major openFDA data categories: adverse events (human/animal), device clearances, drug approvals, shortages, recalls, tobacco, labels, and NDC lookup. It also provides robust analytics via count_values and dataframe queries, plus field discovery. There are no obvious gaps for the stated domain, and the composite drug_profile fills integration needs.

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