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Npi Individual

npi_individual
Read-onlyIdempotent

"Find a doctor / clinician / physician by name" / "look up NPI for [provider]" / "verify a doctor's credentials" / "what's [Dr. X]'s NPI" — search the NPPES National Provider Identifier registry for individual US healthcare providers (~5M entries). Returns NPI numbers + provider names. terms can be a name fragment, NPI, or specialty. Use ef to include address, specialty, gender.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
dfNoComma-separated display fields to use in the `displays` array. Default varies per table; usually the canonical name.
efNoComma-separated extra fields to include per match. Known fields: name.first,name.last,addr_practice.full,licenses.taxonomy.classification.
countNoMaximum matches to return. Default 7, max 500. Use 1–3 for typeahead UX, 20–50 for browsing.
termsYesSearch query — prefix/contains match against canonical names. Whitespace-split into AND tokens. Example: "smith pediatrics".

TDQS

A4.3/5.0
Behavior4/5

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

Annotations declare readOnly, idempotent, and not destructive. The description adds that the registry has ~5M entries, returns NPI numbers and provider names, and that terms can be name, NPI, or specialty. This provides useful behavioral context beyond annotations without contradiction.

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

Conciseness4/5

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

The description is a single, information-dense sentence that starts with example queries, then states the core action, return content, and extra fields. Every part is relevant, though it could be slightly more structured for readability. No wasted words.

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?

Despite no output schema, the description explains what is returned (NPI numbers + provider names) and how to enrich results with 'ef'. For a search tool, this is sufficient for an agent to understand invocation and expected output. The count parameter is documented in schema, so not repeated.

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?

Schema coverage is 100%, so baseline is 3. The description adds value by explaining that 'terms' can be a name fragment, NPI, or specialty (schema only says 'prefix/contains match against canonical names'). It also clarifies that 'ef' includes address, specialty, gender, which is more user-friendly than the schema's field list.

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 searches the NPPES registry for individual US healthcare providers and returns NPI numbers and provider names. It provides example queries (find doctor by name, look up NPI, verify credentials) and distinguishes from siblings via the title 'Npi Individual' and the mention of 'individual' providers, contrasting with sibling 'npi_organization'.

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 gives concrete usage scenarios (find doctor, look up NPI, verify credentials) and explains acceptable input types (name fragment, NPI, specialty). It also advises using 'ef' for extra fields. However, it does not explicitly exclude organizational searches, which would be better handled by the sibling 'npi_organization'.

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

A3.9/5.0
Disambiguation3/5

Several tool groups (ask_pipeworx/ask_pipeworx_grounded/deep_research, conditions/disease_names, polymarket_* family) have overlapping purposes, requiring careful reading of descriptions to differentiate. While many tools are conceptually distinct, the similarity within domains could lead to agent misselection.

Naming Consistency3/5

Naming is mostly snake_case but patterns vary: verb_noun (ask_pipeworx), noun_noun (entity_profile), single verb (forget, recall), and some adjectives (recent_changes). No consistent structure across the set, though individual names are descriptive.

Tool Count2/5

With 40 tools, the server is overly large for typical coherence. The tool count exceeds the 15–25 range deemed borderline, making it hard for agents to navigate and select efficiently.

Completeness4/5

The surface covers medical coding (ICD, LOINC, UCUM, drugs, procedures), financial data, prediction markets, memory, subscriptions, and meta-tools. Missing but minor elements (e.g., CPT codes) do not significantly hinder common workflows.