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search_doctor_registration

Search the live Indian Medical Register by doctor name or registration number to verify a practitioner's registration status. Returns real matches with name_match flags, and flags when the full name may not align.

Instructions

Search the live Indian Medical Register and return real matches.

Provide at least one of doctor_name or registration_number. This calls the
same public JSON endpoint nmc.org.in's own search page uses -- a real, live
lookup, not a guide. That endpoint is undocumented and unsupported by NMC,
so treat a request failure as "try registration_lookup_guide instead," not
as "the doctor doesn't exist."

Quirk worth knowing: NMC's backend 500s on any name value containing a
space (confirmed against the live endpoint -- a bug in their server, not
a validation rule of ours). A multi-word doctor_name is narrowed to its
most distinctive single word before being sent, and every match comes
back with a name_match flag so you can still tell whether the full name
actually lines up.

A registration number match alone doesn't mean the practitioner is
currently in good standing -- always also call check_blacklist.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
doctor_nameNo
state_councilNo
registration_numberNo
year_of_registrationNo

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
sourceYes
cautionYes
matchesYes
returnedYes
truncatedYes
query_noteYes
total_matchesYes
Behavior5/5

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

With no annotations provided, the description carries the full burden of behavioral disclosure. It reveals the endpoint is undocumented and unsupported, the backend 500s on names with spaces, the narrowing workaround, the name_match flag, and the caveat that a registration match alone doesn't imply good standing. This is exceptionally transparent.

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 multi-sentence but every sentence delivers essential information: purpose, usage constraint, failure mode, quirk, and follow-up action. It is well-structured, front-loaded with the core purpose, and avoids fluff or repetition.

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 tool with a live external dependency, undocumented endpoint, and known server bugs, the description covers all necessary operational details: error handling, input quirks, output interpretation (name_match flag), and cross-tool interactions (check_blacklist). Nothing an agent needs to invoke it correctly is missing.

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 description adds meaningful semantics for doctor_name (space handling and narrowing to a distinctive single word) and for registration_number (that a match doesn't imply good standing, requiring check_blacklist). It does not add extra meaning for state_council or year_of_registration, but the schema already provides basic descriptions. Since schema description coverage is 0%, the description compensates for the critical parameters but not all.

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 ('Search'), names the resource ('live Indian Medical Register'), and clarifies it returns 'real matches' rather than a guide. It explicitly contrasts with registration_lookup_guide by stating this is a live lookup, which differentiates it from that sibling.

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 requires 'at least one of doctor_name or registration_number'. It provides clear guidance on failure handling ('treat a request failure as try registration_lookup_guide instead'), and mandates a complementary action ('always also call check_blacklist'). No ambiguity about when to use this 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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