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usa-npn-mcp-server

query-reference-material

Run SQL queries to retrieve reference data that translates natural language requests into specific IDs and terms for querying the NPN API.

Instructions

        Query an SQL database for reference material that can be used to translate natural language into specific ids and terms needed for querying the NPN API with other tools. There is no need to query the 'datasets' table unless specific observer groups are mentioned. The Tables have the following structure:

        Table: species, Length: 1940, Headers: ['species_id', 'common_name', 'genus', 'genus_id', 'genus_common_name', 'species', 'kingdom', 'itis_taxonomic_sn', 'functional_type', 'class_id', 'class_common_name', 'class_name', 'order_id', 'order_common_name', 'order_name', 'family_id', 'family_name', 'family_common_name', 'species_type']
        Description: Contains info on species

        Table: phenophases, Length: 400, Headers: ['definition_id', 'phenophase_id', 'phenophase_name', 'definition', 'start_date', 'end_date', 'dataset_id', 'comments']
        Description: Contains info on phenophases

        Table: phenoclasses, Length: 230, Headers: ['phenophase_id', 'phenophase_description', 'definition_ids', 'phenophase_names']
        Description: Contains info on phenoclasses (a grouping of phenophases)

        Table: datasets, Length: 14, Headers: ['dataset_id', 'dataset_name', 'dataset_description', 'dataset_comments', 'dataset_documentation_url']
        Description: Contains info on datasets and their contributors

        Table: networks, Length: 924, Headers: ['network_id', 'network_name']
        Description: Contains info on observation groups or networks (aka partner groups)

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
sql_queryYesSQL query to run against the SQLite3 database to fetch relevant data.
Behavior2/5

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

No annotations are provided, so the description must disclose behavioral traits. It does not state whether the tool is read-only, required permissions, rate limits, or side effects. The table descriptions hint at a non-destructive query, but it is not explicit.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness3/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is lengthy due to detailed table schemas, which are necessary for writing SQL but could be summarized. The purpose is stated upfront, but the overall structure could be more concise.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness2/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

With no output schema, the description should explain return format and error handling. It lists table headers but does not specify how results are returned or any limitations. For a single-parameter tool, this leaves gaps for an agent.

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% for the single 'sql_query' parameter, so baseline is 3. The description provides table structures that aid in formulating queries, adding practical value beyond the schema's generic description, but does not enhance parameter semantics directly.

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 explicitly states the tool queries an SQL database for reference material to translate natural language into specific IDs and terms for other NPN API tools. It clearly distinguishes itself from sibling tools focused on raw data or phenometrics.

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 advises against querying the 'datasets' table unless specific observer groups are mentioned, providing direct usage guidance. It implies use before other tools but does not explicitly contrast with siblings or state when not to use.

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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