finpay-mcp-server

Server Quality Checklist

Profile completionA complete profile improves this server's visibility in search results.

Latest release: v1.0.0
Disambiguation5/5
Each tool has a clear, distinct purpose: list_* returns collections, get_* returns individual entities by ID, explain_service provides detailed service metadata, and search_docs handles documentation queries. No two tools overlap in function or target resource.
Naming Consistency5/5
All tool names follow a consistent verb_noun pattern in snake_case. List tools use 'list_', retrieval tools use 'get_', plus 'explain_' and 'search_' which are also descriptive verbs. Naming is uniform and predictable.
Tool Count5/5
Eight tools is well within the ideal range for a payment platform query/monitoring server. Each tool covers a needed operation without redundancy or bloat.
Completeness5/5
The server provides complete read-only coverage for its domain: listing and getting hubs, services, and transactions, plus overall system health and documentation search. No obvious gaps for the intended use case; write operations are likely out of scope.
Average 4.3/5 across 8 of 8 tools scored.
See the Tool Scores section below for per-tool breakdowns.
- No community issues in the last 6 months
- 6 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 failing
Add a LICENSE file by following GitHub's guide. Once GitHub recognizes the license, the system will automatically detect it within a few hours.
If the license does not appear after some time, you can manually trigger a new scan using the MCP server admin interface.
MCP servers without a LICENSE cannot be installed.
This repository includes a README.md file.
No tool usage detected in the last 30 days. Usage tracking helps demonstrate server value.
Tip: use the "Try in Browser" feature on the server page to seed initial usage.
Add a glama.json file to provide metadata about your server.
If you are the author, simply .
If the server belongs to an organization, first add glama.json to the root of your repository:
```
{
  "$schema": "https://glama.ai/mcp/schemas/server.json",
  "maintainers": [
    "your-github-username"
  ]
}
```
Then . Browse examples.
Add related servers to improve discoverability.

How to sync the server with GitHub?

Servers are automatically synced at least once per day, but you can also sync manually at any time to instantly update the server profile.

To manually sync the server, click the "Sync Server" button in the MCP server admin interface.

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

Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries the burden of behavioral disclosure. It clearly lists the return contents and explicitly notes that a NOT_FOUND error is returned for nonexistent service names. It does not discuss side effects or permissions, but the 'Get' verb implies a read-only operation, making this acceptable.
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 two concise sentences, front-loaded with the primary purpose and followed by key details and error behavior. No redundant or filler content is present.
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 single-parameter tool with no output schema, the description comprehensively lists all returned data elements and the error condition. It explains the human-readable dependency graph aspect, covering the tool's scope adequately without needing to over-explain.
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?
The schema already provides 100% coverage of the single 'name' parameter, including examples. The description adds the error condition tied to the parameter ('if the service name does not exist') but does not add further semantic detail beyond what the schema offers, so the baseline score of 3 is appropriate.
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 'Get a detailed explanation of a FinPay microservice' and enumerates the specific details returned (purpose, owning team, language, dependencies, health, port). This distinguishes it from sibling tools like list_services or get_system_health, which serve different purposes.
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 this tool is for retrieving detailed information about a specific service, but it does not explicitly state when to use it versus other tools like list_services or get_system_health, nor does it mention any prerequisites such as confirming the service exists first.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries the disclosure burden. It discloses the dependency on DOCU_RAG_URL and the disabled error behavior, as well as the response shape (synthesised answer plus chunks). This goes beyond the schema, though it could specify rate limits or auth, but for a search tool this is sufficient.
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 two sentences, front-loaded with the core purpose, then quickly covers return value and configuration. 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?
Given the tool's simplicity (2 params, no output schema), the description covers purpose, return value, environment dependency, and error condition. It doesn't mention pagination but topK bounds are in the schema. Overall complete for a search tool.
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%, so the schema already documents both query and topK clearly. The description adds no extra parameter meaning beyond what the schema provides; it only references 'top matching chunks' which aligns with topK. Baseline 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 description clearly states 'Full-text search over FinPay architecture documentation' with a specific verb and resource, and outlines the return type (synthesised answer plus top chunks). This distinguishes it from sibling tools that target specific resources like transactions or services.
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 querying architecture documentation but does not explicitly compare to alternatives. It mentions the DOCU_RAG_URL prerequisite, which is useful operational context, but lacks guidance on when to prefer this tool over sibling resource-specific tools.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries the full burden. It discloses what the tool returns (health metrics, supported currencies/countries, limits) and explicitly mentions the NOT_FOUND error for invalid IDs, which is useful behavioral context. It does not mention auth or rate limits, but for a simple read-only getter, this is adequate.
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 two concise sentences. The first sentence front-loads the purpose and return data; the second covers error behavior. Every word earns its place with no 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 simple getter with one parameter and no output schema, the description sufficiently covers input, output, and error cases. It lists all key return fields and the error condition, making it self-contained and complete for its complexity.
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 a clear description and examples for the 'id' parameter. The tool description adds no extra semantic value beyond what the schema already provides; mentioning 'hub ID' in the error case is redundant. Baseline 3 is appropriate.
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 ('Get'), identifies the resource ('a payment hub'), and clearly states the scope ('current details and health metrics'). It lists the exact data returned (status, currencies, countries, limits, latency, success rate), distinguishing it from sibling tools that list hubs or transactions.
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 clearly implies when to use this tool (when you need a single hub's details/health), and the error behavior indicates it's for an existing hub. However, it does not explicitly name alternatives like list_payment_hubs or state exclusions, so it falls slightly short of full guideline clarity.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the full burden. It discloses the return payload (full transaction object with specific fields) and the error behavior (NOT_FOUND if ID doesn't exist). While it doesn't explicitly state it's read-only, 'look up' implies no side effects. This is adequate for a simple get-like tool.
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 fluff. The first sentence states the action and key detail (single, by ID). The second sentence adds valuable return-content and error information. Every word earns its place.
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 one-parameter lookup tool with no output schema and no annotations, the description is complete: it explains the action, the required input, the return contents, and the error condition. There is no missing critical information for an agent to decide to invoke it correctly.
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?
The input schema already provides 100% coverage: the only parameter 'id' has a description with format (TXN-XXXXXXXXX) and is required. The description adds no extra meaning about the parameter beyond 'by its ID', which is redundant. Baseline 3 applies because schema coverage is high and the description doesn't need to compensate.
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 starts with a clear verb and resource: 'Look up a single FinPay transaction by its ID.' The word 'single' and 'by its ID' distinguishes it from the sibling tool list_transactions, which presumably returns multiple transactions. It also specifies the return content (status, amount, currencies, etc.), making the purpose unambiguous.
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 clearly implies the tool is for fetching a specific transaction when you already have its ID. It does not explicitly name alternatives like list_transactions for cases without an ID, but the phrasing 'by its ID' provides context and implies a precondition. No exclusions are stated, but it's enough for clear context.
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, the description carries the burden of disclosing behavior. It implies a read-only operation via the verb 'List' and mentions live health status, but it does not explicitly state absence of side effects, permission requirements, or error behavior. While adequate for a simple list tool, it falls short of full transparency.
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 two sentences, front-loaded with the core purpose and then listing the specific services. Every word earns its place, with no redundancy or fluff.
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 simple zero-parameter tool with no output schema, the description is complete. It specifies the exact scope (all 7 microservices), the content (metadata and current health status), and even enumerates all service names, giving the agent full context to understand the tool's output.
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 tool has zero parameters, so the baseline is 4. The description does not need to explain parameters; it focuses on what the tool returns. It adds no parameter-related semantics because there are none to describe.
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 lists all 7 microservices in the FinPay platform with metadata and current health status. It uses a specific verb ('List') and resource ('microservices'), and enumerates the exact services included, distinguishing it from siblings like list_payment_hubs.
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 makes it obvious when to use this tool: to list all microservices. It provides clear context but does not explicitly mention alternatives or exclusionary conditions, such as using get_system_health for health-only queries. The naming and scope imply usage, but no direct comparison to siblings is given.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the transparency burden. It discloses the output scope (all 4 named hubs) and that it includes status and health metrics, implying a read-only operation. It does not detail error behavior or exact metric fields, but for a parameterless list tool this is adequate.
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 concise sentences that front-load the purpose and then provide useful specifics (the four hubs). No redundant language or unnecessary filler.
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 no-parameter list tool with no output schema, the description is sufficiently complete. It states what is returned and even names all results. Slightly more detail on the exact health metrics would improve it, but the current description is enough for selection and invocation.
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 tool has zero parameters and the schema is empty, so the baseline is 4. The description adds no parameter details, but none are needed; it instead explains the fixed set of hubs, which is more relevant.
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 'List all payment hubs' with 'current status and health metrics', and even enumerates the exact four hubs. This unambiguously defines the resource and verb, and distinguishes from sibling get_payment_hub which targets a single hub.
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 makes it clear this is for an overview of all payment hubs, implying it should be used when a complete listing is needed. It doesn't explicitly mention alternatives like get_payment_hub, but the 'all 4 hubs' phrasing provides sufficient context for when to choose this tool.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the burden. It discloses ordering (createdAt descending), result cap (up to 50), and empty-array behavior when no filters match. It does not precisely define 'recent' (time window) or mention pagination/rate limits, but these are minor for a read-only list tool.
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?
Four short sentences, front-loaded with the purpose ('List recent FinPay transactions'), followed by concrete behavioral details. Every sentence contributes distinct information with no redundancy or fluff.
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?
The tool is simple (3 optional params, no output schema). The description covers the main operational aspects: return type, filters, limit, ordering, and empty result. It doesn't describe the transaction object shape, but that is likely covered by get_transaction and is not essential for a list tool.
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 input schema already describes all three parameters with examples, enum values, and default limits (100% coverage). The description adds the 'and/or' combinability of status and hubId, and ties the limit parameter to the 50-item cap, providing semantic value beyond the schema's individual property descriptions.
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 ('List') and identifies the resource ('FinPay transactions'), along with scope ('recent', up to 50, filters). It clearly distinguishes from siblings like get_transaction (singular) and list_payment_hubs (different resource), making the tool's purpose unambiguous.
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 conveys it is the tool for querying a set of transactions with optional status/hubId filters, and clarifies the 50-limit constraint. It does not explicitly name alternatives or state when not to use it, but the plural 'List' and filter options imply the intended use vs. singular get_transaction. Clear context but no explicit exclusions.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the disclosure burden. It clearly communicates a read-only operation by using 'Get' and 'Returns,' and it details the output (status, per-service, per-hub success, alerts). It does not explicitly state there are no side effects, but the nature of a health snapshot makes this evident.
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 two sentences, front-loaded with the main function, and each sentence adds value: first states what it does, second explains output and usage. No wasted words.
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 simplicity (no params, no output schema), the description is highly complete. It covers the return structure (system status, per-service, per-hub, alerts) and provides usage context. No important aspect 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 tool has zero parameters, and the description correctly omits any parameter information. The schema is trivially 100% covered, and with no params, the description adds no parameter semantics. Baseline is 4.
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 the specific verb 'Get' and clearly identifies the resource ('unified health snapshot of the entire FinPay platform'). It also enumerates the return contents, distinguishing it from sibling tools that focus on individual services or transactions.
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?
It explicitly states 'Use this tool first when triaging incidents,' providing clear context for when to invoke it. However, it does not name alternatives or specify when not to use it, so it falls short of a 5.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

GitHub Badge

Glama performs regular codebase and documentation scans to:

Confirm that the MCP server is working as expected.
Confirm that there are no obvious security issues.
Evaluate tool definition quality.

Our badge communicates server capabilities, safety, and installation instructions.

Card Badge

Copy to your README.md:

[![finpay-mcp-server MCP server](https://glama.ai/mcp/servers/thanhnv2210/finpay-mcp-server/badges/card.svg)](https://glama.ai/mcp/servers/thanhnv2210/finpay-mcp-server)

Score Badge

Copy to your README.md:

[![finpay-mcp-server MCP server](https://glama.ai/mcp/servers/thanhnv2210/finpay-mcp-server/badges/score.svg)](https://glama.ai/mcp/servers/thanhnv2210/finpay-mcp-server)

Latest Blog Posts

Who's Calling? MCP Hosts Are an Identity Blind Spot (And the Spec Knows It)
By Om-Shree-0709 on July 25, 2026.
mcp
Agent Identity
OAuth 2.1
Your AI Chatbot Just Exposed Your CEO's Salary to an Intern
By Om-Shree-0709 on July 2, 2026.
Agent Identity
MCP Security
OAuth Delegation
Why MCP Servers Need Execution Sandboxing (And Why Your Current Stack Isn't Enough)
By Om-Shree-0709 on June 30, 2026.
Agentic Ai
Prompt Injection
WebAssembly

MCP directory API

We provide all the information about MCP servers via our MCP API.

curl -X GET 'https://glama.ai/api/mcp/v1/servers/thanhnv2210/finpay-mcp-server'

If you have feedback or need assistance with the MCP directory API, please join our Discord server