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paleobiology-mcp-server

by io.github.cyanheads

paleobiology-mcp-server: describe staged canvas tables

paleobiology_dataframe_describe
Read-onlyIdempotent

List the tables and their columns staged on a DataCanvas by paleobiology_search_occurrences. Call this before paleobiology_dataframe_query to discover the exact table_name and column names to reference in SQL.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
canvas_idYesCanvas id returned by paleobiology_search_occurrences when its result spilled.

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
errorNoPresent when the call failed. Absent on success.
tablesNoTables staged on the canvas.

TDQS

A4.5/5.0
Behavior4/5

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

Annotations already declare readOnlyHint and idempotentHint, so the safety profile is covered. The description adds useful context about what is listed, where the data comes from, and how the result should be used, which goes beyond the annotations without contradicting them.

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 clear front-loading: the action and resource are stated first, followed by the actionable usage guidance. No filler or redundant restatement of the tool name or title.

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 low-complexity introspection tool with an output schema and strong annotations, the description is complete. It tells the agent what the tool does, where the canvas_id comes from, and how to use the result, leaving no critical gaps.

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 schema already fully describes canvas_id, including its origin from paleobiology_search_occurrences when its result spilled. The description does not add new parameter-level semantics beyond restating the intended use context, so baseline 3 applies given 100% schema coverage.

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 uses a specific verb ('List') and identifies the exact resource ('tables and their columns staged on a DataCanvas'). It also names the preceding tool, paleobiology_search_occurrences, making it clear what the tool does and how it fits into the workflow.

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?

The description explicitly states when to use the tool ('Call this before paleobiology_dataframe_query') and why: to discover table_name and column names for SQL. This gives an agent clear sequencing guidance versus its closest sibling tool.

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

A4.7/5.0
Disambiguation5/5

Each tool targets a clearly distinct resource or workflow: taxa, intervals, occurrences, collections, diversity curves, and staged dataframe inspection. Even the two analysis-oriented tools (get_diversity and dataframe_query) are separated by whether the user needs a precomputed curve or arbitrary SQL over staged results.

Naming Consistency4/5

All tools share the paleobiology_ prefix and use clear snake_case names, so the set is predictable overall. The minor deviation is that dataframe_describe and dataframe_query place the object before the verb, while the other five tools use verb_object ordering (get_diversity, get_taxon, list_intervals, search_collections, search_occurrences).

Tool Count5/5

Seven tools is a well-scoped size for a Paleobiology Database client: two search tools, two lookup tools, two dataframe-analysis tools, and a diversity-curve tool. Each tool earns its place without redundancy or bloat.

Completeness5/5

The surface covers the full read-only workflow: resolve taxa, look up time intervals, search occurrences and collections, compute diversity curves, and run SQL analysis over large staged occurrence sets. No obvious gaps exist for the stated domain; pagination and staging hand-offs create no dead ends.

Resources