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eia-energy-mcp-server

by io.github.cyanheads

Describe EIA Dataframes

eia_dataframe_describe
Read-onlyIdempotent

List canvas dataframes (df_) materialized by eia_query_route calls that passed stage: true, with provenance, expiry, row count, and column schema. Nothing is staged until such a call runs, so an empty list on a fresh session means no query has staged yet, not that staging failed. Drops entries for dataframes the canvas no longer holds before responding, so the list is always current. Pass a specific name to inspect one dataframe; omit to list all active dataframes for this tenant. A name that is not staged comes back as found=false alongside the handles that are, never as an empty list. Listing is not use: only an eia_dataframe_query statement naming a dataframe extends its expiry, so a dataframe polled with this tool and never queried still lapses on schedule.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
nameNodf_<id> handle to describe a single dataframe. Omit to list all active dataframes.

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
errorNoPresent when the call failed. Absent on success.
foundNoTrue when the requested name is staged, false when it is not. Absent when no name was supplied — an unscoped list has nothing to resolve.
dataframesNoDataframes matching the requested scope, newest first. Empty when nothing is staged, or when a supplied name does not resolve — read found and active_names to tell those apart.
active_namesNoEvery df_<id> handle staged for this tenant, regardless of the requested scope. On a miss these are the handles that are still usable.
requested_nameNoEcho of the name input. Absent when no name was supplied.

TDQS

A4.9/5.0
Behavior5/5

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

Beyond the readOnly and idempotent annotations, the description discloses that entries for dataframes no longer held by the canvas are dropped before responding, non-staged names return found=false rather than an empty list, and that polling does not extend expiry. These are critical stateful behaviors an agent must know.

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 densely packed with zero verbosity: each sentence carries new information—purpose, staging prerequisite, parameter usage, error case, and expiry distinction. It is front-loaded with the core function and then layers essential nuances.

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 the tool's complexity (listing/inspecting dataframes with provenance, expiry, schema) and its interaction with sibling tools, the description covers all operational aspects: staging prerequisite, staleness handling, error return semantics, and the expiry distinction from eia_dataframe_query. The presence of an output schema further reduces the need to explain return values, leaving no gap.

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 already documents the name parameter at 100% coverage, and the description repeats the omit-to-list-all behavior. However, it adds meaningful semantics by explaining that a non-staged name returns found=false and that listing does not affect expiry, which goes beyond the schema definition.

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 specific verb (list/inspect) and resource (canvas dataframes), and clearly scopes the tool to dataframes materialized by eia_query_route with stage:true. It explicitly contrasts with eia_dataframe_query on the expiry behavior, distinguishing its role without ambiguity.

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 gives explicit usage guidance: omit the name to list all, provide a name to inspect one, and explicitly states that listing does not extend expiry—only eia_dataframe_query does. It also clarifies that an empty list means no staging has occurred, not failure, which is essential 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

A4.7/5.0
Disambiguation5/5

Each tool serves a clearly distinct function: taxonomy browsing, fuzzy search, route metadata discovery, data retrieval, dataframe inspection, and SQL querying. There is no overlap between the route-focused and dataframe-focused tools, and even the two describe/query pairs are separated by target (route vs. dataframe).

Naming Consistency4/5

All tools share the eia_ prefix and use descriptive verbs, but there's a minor inconsistency: route tools mix plural (browse_routes, search_routes) and singular (describe_route, query_route) forms, while dataframe tools place 'dataframe' before the verb (eia_dataframe_describe, eia_dataframe_query). This is readable and predictable overall, with only slight deviation from a uniform pattern.

Tool Count5/5

With 6 tools, the server is well-scoped for its purpose. It covers discovery, metadata, data access, and SQL analysis without unnecessary bloat or missing essentials. The number fits comfortably within the ideal range for a domain-specific data server.

Completeness5/5

The tool chain is complete for the domain: browse/search to locate routes, describe to get facet/column metadata, query to fetch data, and optionally stage to query with SQL. Dataframe management includes listing and querying, with automatic expiry, so there are no dead ends in the typical workflow. It's a read-only API, but that's appropriate for the energy data use case.

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