tradingagents-mcp

by blurryface1

Overview Schema Related Servers Score Discussions

Python

Local

Server Quality Checklist

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

Latest release: v0.1.0
Disambiguation5/5
Each tool has a distinct role in the analysis lifecycle: capabilities introspection, starting, checking status, retrieving results, listing, canceling, and deleting. There is no overlap or ambiguity between them.
Naming Consistency5/5
All tool names follow a consistent verb_noun pattern (get_capabilities, start_analysis, get_analysis_status, etc.). The naming clearly reflects the action and resource, making the API predictable.
Tool Count5/5
Seven tools is a well-scoped set for a task-management server. Each tool is necessary for the full lifecycle and no redundant tools exist.
Completeness5/5
The tool surface covers the complete analysis workflow: initiation, progress tracking, result retrieval, listing, cancellation, and deletion. Includes capabilities discovery and proper cleanup handling, with no obvious missing operations.
Average 4.6/5 across 7 of 7 tools scored.
See the Tool Scores section below for per-tool breakdowns.
- No community issues in the last 6 months
- 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

Behavior3/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint=true, idempotentHint=true, and destructiveHint=false, covering the safety profile. The description adds context by mentioning pagination and the limit default/max, but it does not disclose ordering, what happens when no status filter is provided, or other behavioral traits. This is adequate but not rich, consistent with the calibration example for similar read-only list tools.
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 plus a parameter list. It front-loads the purpose and then concisely details each argument without unnecessary fluff. Every sentence earns its place.
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 has a simple operation with three optional params and an output schema, so the description does not need to explain the return format. It covers the purpose, pagination, and parameter constraints. However, it does not specify default behavior when no status filter is provided (presumably all statuses) or any ordering, which would make it slightly more complete. Given the simplicity and presence of output schema, this is a minor gap.
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 0% (no descriptions in the schema), and the description fully compensates by explaining each parameter: status with allowed enum values, cursor as a pagination token from previous calls, and limit with default (20) and max (100). This adds substantial meaning beyond the raw type declarations.
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 'List analysis tasks with pagination', identifying the verb (list), resource (analysis tasks), and key behavior (pagination). This distinguishes it from sibling tools like get_analysis_status (single status), start_analysis (creation), and cancel/delete/result (different operations).
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. Usage is implied by the name and the parameter details (e.g., 'cursor from a previous list call' suggests iterative use), but there is no explicit 'use this when' or 'instead of' guidance.
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, idempotentHint, and destructiveHint=false, so safety is covered. The description adds valuable behavior: it returns only a structured summary with rating and resource reference, and points to the resource URI for the full report. This goes beyond the schema and 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 succinct and front-loaded with the purpose. It uses a short intro, a clarifying note about return contents, and a simple Args section. Every sentence adds value with no redundant 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?
Given the tool's simplicity (one parameter), the annotations, and the existence of an output schema, the description covers the essential context: what it returns, what it doesn't return, and that the task must be completed. It sufficiently differentiates from sibling tools and provides enough guidance for correct use.
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 schema only defines run_id as a required string with no description, so the description carries the burden. The line 'Args: run_id: Task identifier for a completed analysis' adds meaningful context about what the parameter represents and the expected state. It does not provide format examples, but that is minor for a single identifier.
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 states a specific verb and resource: 'Get the result summary of a completed analysis task.' It clearly distinguishes from siblings like get_analysis_status (status) and start_analysis (initiate) by focusing on the result summary. The qualifier 'completed' adds precision.
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 this tool should be used after an analysis completes, noting it is for a 'completed analysis task.' It also clarifies that the full markdown report is accessed via a resource URI, guiding the user away from expecting that here. However, it does not explicitly name alternatives like get_analysis_status for checking progress.
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 context: the tool returns non-sensitive configuration, explicitly noting 'no credentials or API keys.' This goes beyond the structured fields, addressing safety and scope concerns.
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 brief and front-loaded: the first sentence states the core purpose, and the second paragraph adds useful specifics without repetition. Every sentence earns its place, including the non-sensitive reassurance.
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 has no parameters, a rich output schema, and strong annotations, the description covers all necessary aspects: what it returns, supported analyses, default parameters, server limits, and security posture. It is fully complete for a capabilities/metadata 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 input parameters, so the baseline is 4. The description appropriately focuses on the output content rather than parameter details, and no parameter semantics 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 uses a specific verb and resource: 'Return the capabilities and configuration of this TradingAgents MCP server.' It clearly identifies the tool's scope and differentiates it from siblings that handle analysis lifecycle, such as start_analysis or list_analyses.
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 implies when to use the tool by stating it 'Describes what analyses are supported, default parameters, and server limits,' positioning it as a discovery/preflight tool before running analyses. It doesn't explicitly name alternatives or exclusions, but the sibling tools are clearly distinct operations and no ambiguity arises.
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, idempotentHint, and destructiveHint=false, so safety is covered. The description adds behavioral context by listing the possible statuses ('queued, running, succeeded, failed, cancelled') and timestamps, which helps the agent understand what the response will contain. It also frames polling behavior, going 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 concise and well-structured: a one-sentence purpose, a detailed return summary, a usage hint, and an Args block. Every sentence adds value. 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 one-parameter polling tool with an output schema and rich annotations, the description is fully adequate. It covers purpose, usage, parameter semantics, and return content. It does not need to explain return structure since an output schema exists.
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?
The schema has only run_id with a title 'Run Id', but no description. The tool description compensates fully by stating 'run_id: Task identifier returned by start_analysis.' This provides provenance and clarifies the parameter's meaning, completely covering the schema's gap.
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 states a specific verb and resource: 'Get the current status of an analysis task.' It clearly distinguishes from siblings like start_analysis (which starts tasks) and get_analysis_result (which likely retrieves results). The action is unambiguous and the scope is well-defined.
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 says 'Use this to poll for completion,' which explicitly tells the agent when to invoke this tool. It does not name alternative tools for exclusions, but the context is clear that polling is the intended use case. This is solid guidance, though it could mention not to use for final results.
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?
Goes beyond annotations by explaining the task transitions to a 'cancelled' terminal state, partial results remain available, and no effect on succeeded/failed tasks. This enriches the destructiveHint and idempotentHint annotations without contradiction.
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?
Description is front-loaded with the purpose, followed by concise behavioral notes and a single parameter explanation. No unnecessary verbosity.
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 simple one-parameter schema, provided annotations, and output schema, the description fully covers the tool's behavior, state transitions, and parameter semantics. No need to describe return values since output schema exists.
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 0% description coverage, but the description defines run_id as 'Task identifier to cancel,' which is sufficient for a single, simple string parameter.
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 'Cancel a queued or running analysis task,' using a specific verb and resource. It distinguishes itself from siblings like start_analysis and delete_analysis.
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 for when cancellation applies (queued/running) and when it has no effect (succeeded/failed). It does not explicitly name alternative tools like delete_analysis, but the guidance is still useful.
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 the annotations (destructive, idempotent), the description discloses what is removed (task record, report, logs, idempotency mapping), confirms irreversibility, and explains the confirmation parameter's purpose. This exceeds what annotations alone provide.
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 a front-loaded purpose, followed by essential context and a clear Args section. Every sentence adds value—no fluff or redundancy—making it appropriately sized for a destructive 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?
Given the tool's destructive nature and single parameter, the description covers all critical aspects: when deletion is allowed, what gets deleted, irreversibility, and the confirmation mechanism. The output schema exists, so return values need no explanation. The ADR reference adds useful organizational 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 schema has one parameter with zero description coverage. The description's Args section provides critical semantics: confirm_run_id must exactly match the run_id to prevent accidental deletion. This meaningfully compensates for the schema gap, though it could have elaborated on how to obtain the run_id.
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 immediately states "Permanently delete an analysis task and all its data" with a specific verb and resource. It distinguishes from siblings like cancel_analysis by specifying only terminal tasks can be deleted, 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 explains when to use the tool (explicit deletion is required, no auto-cleanup per ADR-0009) and constrains usage to terminal tasks. It doesn't explicitly name alternatives like cancel_analysis for active tasks, but the context strongly implies that distinction.
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?
The description adds significant behavioral context beyond annotations: it declares the task is asynchronous, mentions idempotency behavior, and notes the full report is available as an MCP resource. It also imposes a constraint on analysis_date (must not be in the future), which is not present in annotations or schema. This complements the readOnlyHint=false and idempotentHint=true annotations without contradiction.
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: a concise purpose sentence, a clear workflow sentence, and a bulleted Args list. Every sentence adds value, and the format makes it easy for an agent to parse.
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 complexity (6 parameters, async behavior), the description covers purpose, workflow, parameter constraints, and resource availability. An output schema exists, so return values are not needed. The description is complete enough for an agent to start an analysis and know how to retrieve the result.
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 0%, but the description fully compensates by explaining every parameter: symbol with examples, analysis_date with format and constraint, asset_type with valid values and auto-detection, depth with values and defaults, language with values, and idempotency_key with its semantics. This provides far more meaning than the bare 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's action: 'Start a new financial research analysis for a given symbol.' The verb 'start' is specific, and the resource is defined. It also distinguishes itself from sibling tools by explicitly naming get_analysis_status and get_analysis_result for follow-up steps.
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 workflow guidance: 'Use get_analysis_status to check progress and get_analysis_result to retrieve the final summary once the task succeeds.' It names the alternative tools for subsequent actions, making the intended usage pattern clear.
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:

[![tradingagents-mcp MCP server](https://glama.ai/mcp/servers/blurryface1/tradingagents-mcp/badges/card.svg)](https://glama.ai/mcp/servers/blurryface1/tradingagents-mcp)

Score Badge

Copy to your README.md:

[![tradingagents-mcp MCP server](https://glama.ai/mcp/servers/blurryface1/tradingagents-mcp/badges/score.svg)](https://glama.ai/mcp/servers/blurryface1/tradingagents-mcp)

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/blurryface1/tradingagents-mcp'

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