indexa-capital-mcp-server

Server Quality Checklist

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Latest release: v0.1.0
Disambiguation5/5
Each tool targets a distinct aspect of the Indexa Capital API: user profile, account info, portfolio, performance, transactions, fees, and an aggregated summary. Descriptions explicitly state what not to use each tool for, eliminating ambiguity.
Naming Consistency4/5
All tools follow the 'indexa_get_<noun>' pattern except 'indexa_portfolio_summary', which uses 'summary' instead of 'get'. This is a minor deviation, but overall the naming is consistent and predictable.
Tool Count5/5
With 7 tools covering user info, account details, portfolio, performance, transactions, fees, and an aggregated summary, the set is well-scoped for a read-only investment API. No tool feels superfluous or missing.
Completeness4/5
The tool set covers the core read-only operations for an investment account: user profile, account details, portfolio holdings, performance returns, transaction history, and fees. A summary tool aggregates key data. Minor gaps like tax documents or dividend details are acceptable for a read-only API.
Average 4.7/5 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
- No stable releases found
- No critical vulnerability alerts
- No high-severity vulnerability alerts
- No code scanning findings
- CI status not available
This repository is licensed under MIT License.
This repository includes a README.md file.
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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

Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare the tool is read-only and idempotent. The description adds behavioral details: client-side date filtering and pagination, error codes (404, 401/403), and a note about performance. No contradiction with annotations.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness4/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is well-structured with sections (overview, Args, Returns, Examples, Error handling, Note). It is appropriately detailed without being verbose, though some sections could be slightly more concise.
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 complexity (6 parameters, no output schema), the description includes a typical return object shape and performance recommendations. It covers all necessary aspects for an AI agent to use the tool effectively.
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%, and the description adds value with explanations for each parameter (e.g., account_number as 'Indexa account ID', format for dates, default/max for limit). It also notes the client-side filtering behavior, which is not in 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 tool retrieves transaction history for an Indexa account, listing specific transaction types (contributions, withdrawals, fund subscriptions, etc.). It distinguishes itself from sibling tools by focusing on transactions, not account details or portfolio.
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 explicit when-to-use guidance with examples (e.g., 'How much have I contributed this year?') and lists scenarios. It does not explicitly state when not to use, but the context is clear.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior5/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Beyond annotations (readOnlyHint, idempotentHint), description details that it returns one record per quarter, includes asset base, net fee, VAT, effective fee rate, and invoice PDF link. Also explains error handling (404, 401/403) and date filtering logic.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness4/5
Is the description appropriately sized, front-loaded, and free of redundancy?
Well-structured with sections for description, args, returns, examples, and error handling. Somewhat verbose but all sentences contribute value; front-loading is effective.
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?
Despite no output schema, description fully documents return fields for JSON format, explains Markdown summary, and covers filtering behavior and error codes. Complete for an agent to use without ambiguity.
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% with good descriptions, but the description adds semantics like 'quarters that end on or after this date' for date_from and 'quarters that start on or before this date' for date_to, plus examples for account_number and response_format.
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?
Description clearly states 'Retrieve the management fees charged by Indexa Capital on an account' with specific verb and resource. Distinguishes from sibling tools like indexa_get_transactions or indexa_get_performance by focusing exclusively on fees.
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?
Provides concrete examples under 'Use when:' (e.g., 'How much did Indexa charge me in fees last year?') and explains the quarterly billing context. Does not explicitly exclude other tools but the usage is well implied.
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?
Annotations already declare readOnlyHint=true, destructiveHint=false, idempotentHint=true. Description adds important behavioral details beyond annotations: base-100 return interpretation, formula for returns between two points, and warning that projections can be large. No contradictions.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness4/5
Is the description appropriately sized, front-loaded, and free of redundancy?
Description is relatively long but well-structured with sections for purpose, args, returns, examples, and error handling. It is front-loaded with key info. Every sentence serves a purpose; no wasted words. Slightly verbose but necessary for a complex tool.
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?
Despite no output schema, description provides detailed return format for both markdown and JSON, including field descriptions and types. Covers error codes and scenarios. For a 5-parameter tool with optional projections and output format choice, this is very thorough.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters5/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100%, so baseline is 3. Description adds significant value for each parameter: account_number references schema from another tool, dates specify YYYY-MM-DD, include_projections warns about size, response_format explains output differences. This goes beyond what schema provides.
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?
Description clearly states it retrieves historical performance series for an Indexa account, with specific verb 'Retrieve' and resource 'performance series'. It distinguishes from siblings by providing examples and explicitly warning not to use for current value (use indexa_get_portfolio) or transactions (use indexa_get_transactions).
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines5/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
Provides explicit when-to-use scenarios with examples like 'What's my YTD return?' and 'Don't use when' cases, offering specific alternative tools (indexa_get_portfolio, indexa_get_transactions). This gives clear guidance for the AI agent to choose the correct 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?
Annotations already declare readOnlyHint=true and idempotentHint=true. The description adds useful behavioral context by detailing the API response shape and Markdown output, plus mentions dependency on account_number from indexa_get_me. No contradictions.
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 well-structured with clear sections: purpose, API shape, markdown output, args, examples. Every sentence contributes information without redundancy. Front-loaded with key 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?
Given no output schema, the description provides the full JSON response shape and details the Markdown summary format. It covers common use cases with examples. Comprehensive for a portfolio 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?
Schema coverage is 100% and both parameters have detailed descriptions. The description adds extra value by providing an example account number and clarifying the difference between 'markdown' and 'json' output formats 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 clearly states the tool retrieves the current portfolio composition, including total value, cash, and instrument details. It distinguishes from siblings like indexa_get_performance and indexa_get_transactions by explicitly listing what not to use it for.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines5/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides explicit when-to-use examples ('What's my Indexa portfolio worth?') and when-not-to-use examples with alternative tool names ('historical returns → indexa_get_performance'). This gives clear guidance.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior5/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Discloses internal calls to /users/me, /portfolio, and /performance in parallel. Describes return structure (user, accounts, totals). Adds value beyond annotations which already indicate read-only, idempotent, open-world.
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 paragraphs: first for purpose/usage, second for parameter/return. 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?
Despite no output schema, description provides full return structure in JSON format. Internal call details give sufficient behavioral context. Complete for an aggregator 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?
Only one parameter (response_format) with 100% schema coverage. Description restates enum values and default but adds no new semantics beyond 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?
Clearly states it returns a one-shot overview of ALL user's Indexa Capital accounts. Distinguishes from account-specific sibling tools by specifying scope and usage.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines5/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
Explicitly says to use as FIRST tool for open-ended questions and to use account-specific tools only when user specifies one account or aspect. Provides concrete examples.
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?
Annotations already declare readOnlyHint and idempotentHint. The description adds value by specifying the static nature, listing return fields, and including error handling (404, 401/403), which are beyond the annotations.
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 well-structured with clear sections (purpose, exclusions, args, returns, examples, error handling). Each sentence adds value, and the main purpose is front-loaded.
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?
Despite having no output schema, the description provides a detailed JSON response structure and covers error scenarios. With only 2 parameters and good annotations, the description is complete for effective tool usage.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters5/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is 100% with good descriptions, but the description further clarifies parameter usage (e.g., account_number origin, default response_format) and provides a full JSON response example that enriches understanding.
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 ('Retrieve') and resource ('static information about a single Indexa account'), lists specific fields, and explicitly distinguishes from sibling tools by stating what it does not return.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines5/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
Explicitly states when not to use (for portfolio value or performance) and names alternative tools (indexa_get_portfolio, indexa_get_performance). Also provides 'use when' examples.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior5/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnly, non-destructive, idempotent, open world. Description adds important behavioral context: token authenticity via INDEXA_API_TOKEN, detailed return structure, and error handling for 401/403. This goes beyond what annotations provide.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness4/5
Is the description appropriately sized, front-loaded, and free of redundancy?
Description is well-structured with sections (Args, Returns, Examples, Error handling) and front-loads the purpose. Slightly verbose with the JSON example but remains efficient overall.
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 1-parameter tool, the description covers everything: purpose, when to use, return structure (despite no output schema), error handling, and integration with sibling tools. No gaps identified.
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 covers the single parameter fully (response_format with enum and default). Description adds clarity that no input is required beyond the token, and explains the effect of choosing 'markdown' vs 'json'. Adequate but not exceptional beyond 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 it retrieves the authenticated user's profile and list of accounts. It distinguishes itself from siblings by positioning itself as the entry point to discover account numbers needed for other tools.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines5/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
Provides explicit examples of when to use (user asks about accounts, need account_number) and when not to use (user already provided account_number). Also covers the prerequisite for other tools.
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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