tally-mcp-server

Server Quality Checklist

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

Latest release: v0.1.0
Disambiguation5/5
Each tool targets a distinct Tally entity: companies, ledgers, groups, stock items, vouchers, and balance status. There is no overlap in the data returned, so an agent can easily select the right tool.
Naming Consistency4/5
Most tools follow a list_* pattern (list_companies, list_ledgers, list_groups, list_stock_items), but day_book and outstanding_balances deviate. Despite this, the names remain descriptive and predictable.
Tool Count5/5
Six tools is a well-scoped set for a TallyPrime integration, covering the primary read operations without being sparse or overwhelming.
Completeness4/5
The tool set provides comprehensive read-only coverage of accounting entities: masters, transactions, and balances. Minor gaps exist, such as no voucher-level detail beyond day_book, but the core reporting needs are met.
Average 4.1/5 across 6 of 6 tools scored.
See the Tool Scores section below for per-tool breakdowns.
- No community issues in the last 6 months
- 3 commits in the last 12 weeks
- No stable releases found
- No critical vulnerability alerts
- No high-severity vulnerability alerts
- No code scanning findings
- CI status not available
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

Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries full responsibility for behavioral disclosure. It states the operation (returns vouchers) but does not disclose return format, ordering, pagination, or inclusion/exclusion criteria (e.g., whether cancelled vouchers are included).
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, front-loaded sentence that directly states the action and scope. Every word is informative, with no filler or repetition.
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 read-only list tool with fully documented parameters, the description is sufficient to understand the tool's core function. However, without an output schema or annotations, it could be richer by indicating result structure or limitations, but this is not critical.
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 describes both fromDate and toDate with patterns and examples, achieving 100% coverage. The description only rephrases the date range without adding new parameter semantics beyond the schema.
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 'Returns' and explicitly names the resource 'vouchers' with scope 'between two dates'. It clearly distinguishes from sibling tools like list_ledgers or outstanding_balances, 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 the tool is for retrieving vouchers in a date range but does not explicitly state when to prefer it over alternatives or provide exclusions. There is no guidance on use cases such as 'for day book reports' or 'instead of listing ledgers'.
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. The word 'Returns' implies a read-only operation and 'all' indicates full scope, but it does not disclose authentication needs, company scope, or output format. This is sufficient for a simple list tool but lacks detailed 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?
The description is a single sentence that is front-loaded with the main action ('Returns all accounting groups') and enriched by relevant examples. Every word earns its place with no redundancy or 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 zero-parameter, no-output-schema tool, the description adequately defines what is returned and provides illustrative examples. It is slightly incomplete in not clarifying whether results are scoped to a specific company or describing the return shape, but overall it gives enough context for a simple 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 tool has zero parameters, and schema coverage is 100% (empty schema). Per guidelines, the baseline for 0 parameters is 4. The description adds no parameter-specific semantics, but none are needed.
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 'Returns all accounting groups (chart-of-accounts categories) from TallyPrime' and gives concrete examples like 'Sundry Debtors, Bank Accounts, Direct Expenses'. This specific verb+resource combination distinguishes it from sibling list tools such as list_ledgers and list_companies.
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 when to use the tool (when you need accounting groups) but does not explicitly discuss alternatives or exclusion criteria. Unlike the high benchmark, it does not mention 'use list_ledgers for ledgers' or similar, leaving usage guidance to inference.
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 full burden. It indicates a read operation via 'Returns' and offers group options, but it does not disclose potential edge cases such as empty results, sorting, or error behavior. This is a moderate level of 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?
Two concise sentences, front-loaded with the primary action and additional usage detail. Every sentence contributes value, with 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 one-parameter read tool, the description provides essential information: return type, group selection, and example usage. It lacks output structure details but that is acceptable given the implied return list.
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 covers the parameter 'group' with a full description matching the tool description, so the description adds little new semantic value. Baseline is 3 due to high schema coverage.
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 'Returns' and identifies the resource 'ledgers in a given account group' with the additional detail of 'current closing balance.' It clearly distinguishes from sibling tools like list_ledgers by focusing on balances within a group.
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 states the default group (Sundry Debtors) and instructs how to get payables via 'Sundry Creditors,' providing clear context. It does not explicitly name alternative tools or state when not to use it, but the guidance is sufficient for a simple query 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?
Without annotations, the description carries the burden of transparency. It explicitly indicates a read operation ('Returns') and adds context about the data included (opening and closing balances, account types). It does not disclose limitations like pagination or company filtering, but for a simple list tool, the description is sufficiently 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 a single, well-structured sentence that front-loads the main action and provides essential detail (account types and balances) without any fluff or repetition.
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 description is mostly complete for a no-parameter list tool, but it does not clarify whether the ledger accounts are for the current company or all companies, which is relevant given a sibling tool list_companies exists. This minor ambiguity prevents a perfect score.
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 has no parameters, so there are no parameter semantics to clarify. The description adds no parameter-specific information, but the absence of parameters means the baseline of 4 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 the tool 'Returns all ledger accounts' and lists specific types (parties, banks, cash, expense/income heads), distinguishing it from sibling tools like list_companies or list_stock_items. The verb 'Returns' and resource specification are precise, 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 Guidelines3/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description does not explicitly provide when-to-use or exclusions, but the clear purpose implies usage when ledger accounts with balances are needed. Sibling tools are not mentioned, so no direct alternatives are referenced, but the intended use is reasonably inferred.
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 the full burden of behavioral disclosure. It states that the tool returns a list, implying a read-only operation, and mentions the 'connected' instance as a prerequisite. However, it does not describe potential error conditions, data format, or behavior if the connection is inactive.
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, clear, well-structured sentence that covers the essential information without unnecessary words. It is front-loaded with the verb and resource.
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 list tool, the description is complete: it states what the tool returns and in what context. There is no missing information that would prevent an agent from invoking it correctly.
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 coverage is 100% (vacuously). The description does not need to add parameter semantics, and the baseline for zero parameters is 4, which is appropriate here.
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 action ('Returns') and the specific resource ('list of companies'), and distinguishes it from sibling tools that handle other entities like ledgers, groups, and stock items. It also provides context ('connected TallyPrime instance') that anchors the scope.
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 provides clear context on when to use the tool: when you need the list of companies in the connected TallyPrime instance. It does not explicitly mention alternatives or exclusions, but the resource name and phrasing make its usage straightforward.
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 full transparency burden. It clearly indicates a read-only operation ('Returns') and specifies the return fields. It does not mention potential caveats like pagination, latency, or failure modes, but for a zero-parameter list tool with a straightforward purpose, the disclosure 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 a single sentence that is front-loaded with the action and resource. It contains no redundant words and earns its place by naming the specific data fields returned. This is a model of conciseness.
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 list tool with no output schema, the description is sufficiently complete. It tells the user exactly what the tool returns and from where. The absence of an output schema is compensated by explicitly naming the return fields. No additional context is needed.
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 per the rubric the baseline is 4. The description does not need to add parameter semantics because there are none. It correctly focuses on the output instead.
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 ('Returns') and clearly identifies the resource ('inventory stock items from TallyPrime') along with the fields returned (closing quantity, rate, value). It clearly distinguishes from sibling tools like list_ledgers or list_companies by naming a distinct resource type.
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 does not explicitly state when to use this tool versus alternatives, nor does it mention exclusions or alternative tools. However, the resource type is clear enough that the usage context is implied: use when needing stock items. No explicit when-to-use guidance is provided, so it does not reach level 4.
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:

[![tally-mcp-server MCP server](https://glama.ai/mcp/servers/tejash-bhandari-dev/tally-mcp-server/badges/card.svg)](https://glama.ai/mcp/servers/tejash-bhandari-dev/tally-mcp-server)

Score Badge

Copy to your README.md:

[![tally-mcp-server MCP server](https://glama.ai/mcp/servers/tejash-bhandari-dev/tally-mcp-server/badges/score.svg)](https://glama.ai/mcp/servers/tejash-bhandari-dev/tally-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/tejash-bhandari-dev/tally-mcp-server'

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