india-business-mcp-server

Server Quality Checklist

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Latest release: v1.0.0
Disambiguation5/5
Each tool targets a distinct function: GST calculation, pincode serviceability, IFSC lookup, pincode details, GSTIN validation, and PAN validation. Despite some semantic overlap between lookup_pincode and check_pincode_serviceability, the descriptions clearly differentiate them, so no ambiguity.
Naming Consistency5/5
All tool names follow a consistent verb_noun pattern in snake_case (e.g., validate_gstin, lookup_ifsc). While verbs vary (calculate, check, lookup, validate), the pattern is uniform and predictable, enhancing readability and agent understanding.
Tool Count5/5
With 6 tools, the server is well-scoped for its purpose of supporting Indian business operations. Each tool serves a unique and necessary function without redundancy, and the count is appropriate for the domain.
Completeness4/5
The tool set covers core Indian business needs: tax (GST), identification (PAN, GSTIN), banking (IFSC), and postal (pincode). However, it lacks tools for company registration (CIN) or other common identifiers, leaving minor gaps that a broader business verification server might address.
Average 3.8/5 across 6 of 6 tools scored.
See the Tool Scores section below for per-tool breakdowns.
- 0 of 1 community issues answered or closed in the last 6 months
- 13 commits in the last 12 weeks
- No stable releases found
- No critical vulnerability alerts
- No high-severity vulnerability alerts
- No code scanning findings
- CI is passing
This repository is licensed under MIT License.
This repository includes a README.md file.
No tool usage detected in the last 30 days. Usage tracking helps demonstrate server value.
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How is the quality score calculated?

The overall quality score combines two components: Tool Definition Quality (70%) and Server Coherence (30%).

Tool Definition Quality measures how well each tool describes itself to AI agents. Every tool is scored 1–5 across six dimensions: Purpose Clarity (25%), Usage Guidelines (20%), Behavioral Transparency (20%), Parameter Semantics (15%), Conciseness & Structure (10%), and Contextual Completeness (10%). The server-level definition quality score is calculated as 60% mean TDQS + 40% minimum TDQS, so a single poorly described tool pulls the score down.

Server Coherence evaluates how well the tools work together as a set, scoring four dimensions equally: Disambiguation (can agents tell tools apart?), Naming Consistency, Tool Count Appropriateness, and Completeness (are there gaps in the tool surface?).

Tiers are derived from the overall score: A (≥3.5), B (≥3.0), C (≥2.0), D (≥1.0), F (<1.0). B and above is considered passing.

Tool Scores

Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations provided, so description must fully disclose behavior. It states validation and extraction but does not specify behavior on invalid input (error vs flag) or any side effects. For a validation tool, this is a gap.
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?
Single sentence, front-loaded with purpose and extraction detail. Every word adds value, no redundancy.
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?
For a simple validation tool with one parameter and no output schema, the description covers purpose and core behavior (extraction). Minor gap: no mention of return format, but extraction hint compensates.
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% with the single parameter 'pan' described. The description adds no new semantic information beyond the schema, meeting the baseline.
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 validates PAN format and extracts holder type, using specific verb ('validates', 'extracts') and resource ('Permanent Account Number'). It distinguishes from sibling tools like validate_gstin which validate different identifiers.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines3/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
Usage is implied by the tool name and description but no explicit guidance on when to use vs alternatives. Siblings are different enough that confusion is unlikely, but the description lacks direct comparison or exclusion notes.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior3/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description bears full responsibility. It describes the core logic (breakdown calculation, inter/intra-state routing) but omits behavioral details like validation requirements, error handling, or side effects. Adequate but not comprehensive.
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 a single, well-structured sentence that front-loads the purpose and key functionality. No redundant words; every phrase adds value.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness3/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
The tool has moderate complexity (5 parameters, no output schema). The description explains inputs and routing logic but lacks details about output structure or return format. While sufficient for basic use, it leaves gaps for an agent unfamiliar with GST.
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?
Input schema has 100% parameter coverage with descriptions. The description adds context (seller's GSTIN, buyer's location, inter-state routing) but does not enhance individual parameter semantics beyond the schema. Baseline score of 3 applies.
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 verb 'Calculates' paired with the resource 'GST (CGST, SGST, IGST) breakdown' clearly defines the tool's function. It distinguishes itself from sibling tools (validation/lookup) by being a calculation tool.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines3/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description implies usage for GST calculation based on transaction details, but no explicit when-to-use or when-not-to-use guidance is provided. It does not mention alternatives or exclusions, leaving the agent to infer context from sibling names.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior3/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description must carry the full burden. It explains that serviceability is based on allowed states/districts but does not disclose how the parameters interact (e.g., AND vs OR logic), error handling for invalid pincodes, or side effects. The description provides basic but incomplete behavioral context.
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 no redundancy. The purpose and use case are stated upfront. Every word adds value.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness3/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
With no output schema and moderate parameter count (3), the description is insufficient. It lacks details on return format, error scenarios, and how allowedStates and allowedDistricts combine (e.g., union or intersection). For a serviceability check in e-commerce, agents need more complete guidance.
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 description coverage is 100%, so baseline is 3. The description does not add meaning beyond the schema—it merely restates that the check is 'based on allowed states or districts'. No elaboration on parameter constraints or interactions.
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 verb 'checks' and the resource 'Indian PIN code serviceability', specifying it uses lists of allowed states or districts. It explicitly mentions the e-commerce checkout use case, distinguishing it from siblings like lookup_pincode.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines3/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides a use case ('e-commerce checkout and shipping verification') but does not explicitly state when not to use this tool or what alternatives exist among siblings (e.g., lookup_pincode). The guidance is implied rather than explicit.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior3/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the full burden. It discloses caching behavior, which is a positive. However, it does not mention rate limits, data freshness, error handling, or any potential side effects beyond cache optimization.
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 extremely concise with two sentences, front-loading the purpose and key detail (caching). Every word adds value without unnecessary elaboration.
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?
Given the tool's simplicity (one parameter, no output schema), the description adequately covers purpose and caching. It lists the types of location details returned, which compensates for the lack of an output schema. A 5 would require mention of error states or source authority.
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% (one parameter with schema description). The description adds no further semantics beyond the schema's explanation of the pincode parameter, so it meets the baseline but does not exceed it.
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 retrieves location details for an Indian postal PIN code, listing specific output fields (Post Office name, Block, etc.). It uses a specific verb 'Retrieves' and resource 'location details for an Indian Postal PIN code', which distinguishes it from siblings like 'check_pincode_serviceability' that likely serve a different purpose.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines3/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description mentions caching for optimization but does not provide explicit guidance on when to use this tool versus alternatives like 'check_pincode_serviceability'. Usage context is implied but not clearly delineated.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior3/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations provided, so description carries full burden. It mentions caching to optimize response times, a behavioral trait. However, it does not disclose error handling, rate limits, or behavior for invalid IFSC codes.
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: first sentence states purpose and outputs, second mentions caching. Efficient and front-loaded with essential information, 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?
For a simple tool with one parameter and no output schema, the description covers purpose, key outputs, and caching behavior. It could mention error responses or return format, but is adequate.
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 single parameter 'ifsc' has 100% schema description coverage. The description adds format context (11-character, example) and the data retrieved, going beyond the schema's basic description.
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 retrieves details about an Indian bank branch using IFSC code, listing specific fields like Bank Name, Branch, Address, etc., and distinguishes it from sibling tools that handle GST, pincode, PAN, etc.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines3/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description implies usage when an IFSC code is known, but does not explicitly state when to use this tool vs alternatives or provide when-not-to-use conditions. Sibling tools are different in domain, so confusion is low.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior3/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries full burden. It discloses the validation and extraction behavior but lacks details on side effects, rate limits, or authentication needs. For a simple validation tool, this is adequate but not superior.
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?
A single sentence that conveys all essential information without unnecessary words. Perfectly front-loaded with the main purpose.
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 one parameter and no output schema, the description fully covers what the tool does and what it returns. No gaps remain given the tool's simplicity.
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%, but the description adds value by explaining that the tool extracts State, PAN, Entity Type, and Checksum status, and provides an example format. This goes beyond the schema's type and pattern constraints.
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 validates structure, format, and checksum of a GSTIN and extracts components. This clearly distinguishes it from sibling tools like validate_pan or check_pincode_serviceability.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines3/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description implies usage for validating GSTINs but provides no explicit guidance on when to use this tool versus alternatives, nor any exclusions or prerequisites.
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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