CSE MCP Server

by Gajarthan

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Server Quality Checklist

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Latest release: v2.0.1
Disambiguation5/5
Each tool has a clearly distinct purpose: get_index_summary for index data, get_market_status for session status, get_market_summary for market-wide metrics, get_stock_quote for individual stock details, get_top_gainers/losers for performance rankings, and search_company for company lookup. No overlap or ambiguity exists between these functions.
Naming Consistency5/5
All tools follow a consistent verb_noun pattern with 'get_' or 'search_' prefixes (e.g., get_index_summary, search_company). The naming is uniform, predictable, and clearly indicates each tool's action and target resource without any stylistic deviations.
Tool Count5/5
With 7 tools, the server is well-scoped for providing stock exchange data. Each tool serves a specific, non-redundant function, covering key areas like market overviews, individual quotes, rankings, and search. This count is appropriate and efficient for the domain.
Completeness4/5
The toolset covers essential read-only operations for market data: summaries, status, quotes, rankings, and search. Minor gaps exist, such as no historical data retrieval or portfolio management tools, but these are not critical for the stated purpose of providing current CSE information. Agents can perform core tasks without dead ends.
Average 3.2/5 across 7 of 7 tools scored.
See the Tool Scores section below for per-tool breakdowns.
- No community issues in the last 6 months
- 0 commits in the last 12 weeks
- Last stable release on March 31, 2026
- No critical vulnerability alerts
- No high-severity vulnerability alerts
- No code scanning findings
- CI is passing
This repository is licensed under ISC License.
This repository includes a README.md file.
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{
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  ]
}
```
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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?
With no annotations provided, the description carries the full burden of behavioral disclosure. It states the tool returns 'concise index summaries,' which implies a read-only operation, but doesn't clarify aspects like data freshness, rate limits, error conditions, or authentication needs. This leaves significant gaps in understanding how the tool behaves beyond its basic function.
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, efficient sentence that directly states the tool's purpose without unnecessary words. It's front-loaded and wastes no space, making it easy for an agent to parse quickly.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness2/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the lack of annotations and output schema, the description is incomplete for effective tool use. It doesn't explain what the 'concise index summaries' contain, their format, or any behavioral traits like performance or limitations. For a tool with no structured metadata, this leaves too much undefined for reliable agent invocation.
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 has 100% description coverage, clearly documenting the optional 'index' parameter with enum values and a default. The description adds minimal value by mentioning the same indices but doesn't provide additional semantic context beyond what the schema already covers, meeting the baseline for high schema coverage.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose4/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the action ('Return') and resources ('ASPI, S&P SL20, or both as concise index summaries'), making the tool's purpose understandable. However, it doesn't explicitly differentiate from sibling tools like get_market_summary, which might overlap in functionality, preventing a perfect score.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides no guidance on when to use this tool versus alternatives like get_market_summary or get_market_status. It mentions what the tool does but offers no context for selection among siblings, leaving the agent to infer usage scenarios.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
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 states the tool returns data but doesn't describe important behavioral traits such as whether it's real-time or historical data, rate limits, authentication requirements, error conditions, or response format. The description is minimal and lacks essential operational 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, efficient sentence that immediately states the tool's purpose without any wasted words. It's appropriately sized for a simple data retrieval tool and front-loads the essential information about what data is returned.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness2/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's simplicity (zero parameters) but lack of annotations and output schema, the description is insufficiently complete. It doesn't explain the return format, data freshness, or how this market-wide summary differs from sibling tools. For a financial data tool that agents will need to use appropriately, more contextual information would be helpful.
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 with 100% schema description coverage, so the schema already fully documents the parameter situation. The description appropriately doesn't discuss parameters, maintaining focus on what the tool returns rather than what it accepts. This meets the baseline expectation for a zero-parameter tool.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose4/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool's purpose with a specific verb ('Return') and resource ('concise market-wide...data'), listing the exact data types returned (volume, turnover, trade-count, trade-date). However, it doesn't explicitly differentiate from sibling tools like get_index_summary or get_market_status, which likely provide related but different market data.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides no guidance on when to use this tool versus alternatives. It doesn't mention sibling tools like get_index_summary or get_market_status, nor does it specify use cases, prerequisites, or exclusions for this market-wide summary tool.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries full burden but offers minimal behavioral context. It mentions 'normalized price' and 'timestamps' but doesn't explain what normalization entails, data freshness, rate limits, authentication needs, or error conditions. This leaves significant gaps for a tool returning financial data.
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, efficient sentence that front-loads the core purpose. Every word contributes meaning without redundancy, making it appropriately sized for this simple tool.
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?
For a simple read-only tool with one optional parameter and no output schema, the description is minimally adequate. However, it lacks details about data sources, update frequency, or example outputs that would help an agent understand what to expect, especially given the absence of annotations.
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 the parameter 'limit' is fully documented in the schema. The description adds no additional parameter information beyond what the schema provides, meeting the baseline for high schema coverage.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose4/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the action ('Return') and resource ('top CSE gainers') with specific data fields (price, percentage change, timestamps). It distinguishes from siblings like 'get_top_losers' by specifying gainers, but doesn't explicitly differentiate from other market data tools like 'get_market_summary'.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
No guidance is provided on when to use this tool versus alternatives like 'get_top_losers' for losers, 'get_market_summary' for broader data, or 'search_company' for specific stocks. The description only states what it does, not when it's appropriate.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
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 what data is returned but doesn't mention performance characteristics (e.g., real-time vs. delayed data), rate limits, authentication requirements, or error conditions. For a financial data tool with no annotations, this leaves significant behavioral gaps.
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, efficient sentence that front-loads the core purpose and includes key data fields. Every word earns its place with no wasted text, making it easy to parse quickly.
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?
Given the tool's moderate complexity (financial data retrieval with one parameter), no annotations, and no output schema, the description is minimally adequate. It covers what data is returned but lacks details on data freshness, source, format, or error handling. It meets basic requirements but leaves room for improvement.
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 the schema fully documents the single parameter (limit). The description doesn't add any parameter-specific information beyond what's in the schema, such as typical usage patterns or implications of different limit values. Baseline 3 is appropriate when schema does the heavy lifting.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose4/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the verb ('Return') and resource ('top CSE losers') with specific data fields (normalized price, percentage change, timestamps). It distinguishes from some siblings like get_market_status or get_stock_quote but doesn't explicitly differentiate from get_top_gainers, which is a direct counterpart.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
No guidance is provided on when to use this tool versus alternatives. It doesn't mention when to prefer this over get_top_gainers for loser data, or how it differs from get_market_summary which might include similar information. The description lacks any usage context or exclusions.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries full burden. It mentions 'ranked matching' which adds useful behavioral context about result ordering. However, it doesn't disclose critical traits like pagination, rate limits, authentication needs, error conditions, or what the return format looks like (especially without an output schema). For a search tool with zero annotation coverage, this leaves significant gaps.
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, zero waste. Front-loaded with clear purpose, includes resource scope and method. Every word earns its place with no redundant information or fluff. Perfectly concise for a simple search tool.
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?
For a single-parameter search tool with 100% schema coverage but no annotations and no output schema, the description is minimally adequate. It covers the basic purpose and method, but lacks details about return format, error handling, or behavioral constraints that would be needed for full completeness. The absence of output schema means the description should ideally explain what results look like, but it doesn't.
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%, with the single parameter 'query' well-documented in the schema as 'Company name or ticker symbol to search for.' The description adds the context of 'ranked matching' which helps interpret how the query is used, but doesn't provide additional syntax, format examples, or constraints beyond what the schema already states. Baseline 3 is appropriate when schema does the heavy lifting.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose4/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the action ('Search'), target resource ('Colombo Stock Exchange companies'), and method ('by name or symbol using ranked matching'). It distinguishes from siblings like get_stock_quote (specific quote) or get_top_gainers (predefined list), but doesn't explicitly differentiate them. Purpose is specific but sibling differentiation is implied rather than explicit.
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 searching for companies by name or symbol, but provides no explicit guidance on when to use this versus alternatives like get_stock_quote (for specific symbols) or get_top_gainers/losers (for performance lists). No when-not-to-use instructions or prerequisites are mentioned. Usage context is clear but alternatives are not addressed.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
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 states the tool returns current status but lacks details on data freshness, rate limits, error conditions, or response format, which are important for a real-time market data 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?
The description is a single, efficient sentence that directly states the tool's purpose without any redundant or verbose language. It is appropriately sized and front-loaded with essential information.
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?
Given the tool's simplicity (0 parameters, no output schema, no annotations), the description is adequate but has gaps. It explains what the tool does but lacks behavioral context like response format or reliability details, which would be helpful for an agent using this in a financial context.
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 0 parameters with 100% schema description coverage, so no parameter documentation is needed. The description appropriately does not discuss parameters, earning a baseline score of 4 for not adding unnecessary information.
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 specific action ('Return') and resource ('current Colombo Stock Exchange trading session status'), distinguishing it from siblings like get_index_summary or get_market_summary by focusing specifically on session status rather than broader market data.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
No guidance is provided on when to use this tool versus alternatives. It does not mention prerequisites, timing considerations, or differentiate from similar tools like get_market_summary, leaving the agent to infer usage context.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
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. While it states the tool retrieves a 'normalized stock quote' and lists data fields, it doesn't cover critical behavioral aspects such as rate limits, authentication requirements, error handling, or whether the data is real-time or delayed. For a read operation with zero annotation coverage, this leaves significant gaps in 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 a single, well-structured sentence that efficiently conveys the tool's purpose, scope, and included data fields without any redundant or unnecessary information. It's appropriately sized and front-loaded, with every element serving a clear purpose in helping the agent understand the tool.
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?
Given the tool's moderate complexity (single parameter, read operation) and lack of annotations or output schema, the description is partially complete. It covers the basic purpose and data fields but misses behavioral details like response format, error cases, or performance characteristics. For a tool with no output schema, more information about return values would be beneficial, though the listed data fields provide some 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%, with the parameter 'symbol' fully documented in the schema (including type, constraints, and example). The description adds minimal value beyond the schema by specifying 'CSE symbol' and implying it's for a single symbol, but doesn't provide additional syntax, format details, or usage context that isn't already covered in the structured 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 clearly states the specific action ('Get a normalized stock quote') and resource ('for a single CSE symbol'), distinguishing it from siblings like get_index_summary or get_market_summary by focusing on individual stock data. It explicitly lists the included data fields (price movement, volume, turnover, market cap, beta values), making the purpose highly specific and differentiated.
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 retrieving detailed quote data for a single CSE symbol, but provides no explicit guidance on when to use this tool versus alternatives like search_company or get_top_gainers. It mentions the symbol type (CSE) which offers some context, but lacks clear when/when-not instructions or named alternatives for different use cases.
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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