Agentic Prompt
Server Details
1,177 free agentic trading prompts for Claude and Robinhood MCP.
- Status
- Healthy
- Last Tested
- Transport
- Streamable HTTP
- URL
Available Tools
4 toolsget_promptAInspect
Get the full details of a single prompt by its ID, including the complete prompt text, use case, how it works, tags and prerequisites.
| Name | Required | Description | Default |
|---|---|---|---|
| id | Yes | Prompt ID e.g. p-1, p-42, p-1177 |
TDQS
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the full burden and discloses what data is returned (prompt text, use case, how it works, tags, prerequisites). It does not mention side effects, auth requirements, or rate limits, but for a simple read operation the description is sufficiently transparent. It could be improved by explicitly stating it is read-only, but the name and content imply that.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, well-structured sentence that is front-loaded with the action and resource. Every word contributes meaning, and there is no redundancy or filler. It is concise without sacrificing clarity.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's simplicity (1 required parameter, no output schema, no annotations, no nested objects), the description is complete. It explains what the tool does, how to use it, and what to expect in the result. The sibling tools provide additional context for differentiating similar tools.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The input schema has 100% coverage for the single parameter 'id' with a clear example (e.g., p-1). The tool description adds meaning by listing the output fields that correspond to the parameter, helping the agent understand what it will receive. This adds value beyond the schema alone.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the verb 'Get' and the resource 'full details of a single prompt by its ID', and lists specific fields returned (prompt text, use case, how it works, tags, prerequisites). It effectively distinguishes from sibling tools like get_prompts_by_category by emphasizing retrieval by a single ID.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description implies usage when a specific prompt ID is known, which is appropriate for this tool. It does not explicitly state when not to use or compare to alternatives, but the sibling tool names provide sufficient context for differentiation. A slight improvement would be to add direct guidance, but the current level is adequate.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
get_prompts_by_categoryBInspect
Get prompts in a specific category.
| Name | Required | Description | Default |
|---|---|---|---|
| limit | No | Max results (default 20) | |
| category | Yes | Exact category name |
TDQS
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations provided, and the description only states the basic purpose. It does not disclose if the operation is read-only, pagination details, ordering, or behavior when category is missing. The agent lacks critical behavioral cues.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, front-loaded sentence with no unnecessary words. It is concise, though it could be slightly more informative without becoming verbose.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's simplicity (2 parameters, no output schema, no annotations), the description is minimally adequate but lacks details on edge cases, default behavior, or result format. It meets the basic requirement but has clear gaps.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100%, and the description does not add meaning beyond what the schema already provides for the two parameters. Baseline score of 3 is appropriate.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool gets prompts in a specific category. It distinguishes from siblings like get_prompt (single prompt), list_categories (list categories), and search_prompts (search across prompts).
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
No guidance on when to use this tool versus alternatives, nor any exclusions or prerequisites. The usage context is only implied through the tool name and description.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
list_categoriesAInspect
List all 15 prompt categories with their prompt counts.
| Name | Required | Description | Default |
|---|---|---|---|
No parameters | |||
TDQS
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the full burden. It states it lists categories with counts, which is adequate for a simple read tool, but does not mention any behavioral aspects like ordering or caching.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single sentence that is front-loaded with the key action and resource, with no unnecessary words.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
With no output schema, the description must serve as the sole specification. It covers the purpose and result (list with counts), but could optionally mention the exact structure. However, for a simple tool it is nearly complete.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
There are no parameters, so the description does not need to explain them. It adds meaning by specifying 'all 15 prompt categories with their prompt counts', which goes beyond just the name. Baseline for 0 parameters is 4.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description uses a specific verb 'List' and resource 'all 15 prompt categories with their prompt counts', clearly distinguishing it from siblings like get_prompts_by_category which retrieves prompts for a category.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description does not provide any guidance on when to use this tool vs alternatives like get_prompts_by_category. It only states what it does without context.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
search_promptsAInspect
Search agentic trading prompts by keyword, category, or tag. Returns matching prompts with ID, title, use case and direct URL.
| Name | Required | Description | Default |
|---|---|---|---|
| tag | No | Filter by tag (e.g. 'Execution', 'NVDA', 'Healthcare') | |
| limit | No | Max results to return (default 10, max 50) | |
| query | No | Keyword to search in prompt title and content | |
| category | No | Filter by category (e.g. 'Options Strategies', 'DCA & Dip Buying') |
TDQS
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description must bear the full burden. It does not disclose behaviors such as rate limits, authentication requirements, what happens when no matches are found, or whether filters are combined with AND/OR. The return fields are mentioned, but edge cases are ignored.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is two sentences, front-loads the action and filters, and contains no unnecessary words or redundancy. Every sentence adds value.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
The tool has 4 optional parameters and no output schema. The description explains what fields are returned, which is helpful. However, it does not cover behavior for empty results, pagination (though limit is explained), or filter combination logic. Sibling tools exist but are not referenced. Adequate but with clear gaps.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100% (all 4 parameters have descriptions in the schema). The tool description does not add semantic context beyond what the schema already provides; it merely restates the filter dimensions. Baseline of 3 is appropriate.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool's action ('Search'), the resource ('agentic trading prompts'), and the filtering dimensions ('by keyword, category, or tag'). It also specifies the return fields (ID, title, use case, direct URL), distinguishing it from sibling tools like get_prompt which retrieves a single prompt.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description implies usage when you have keyword, category, or tag to filter by, but does not explicitly state when to use alternatives (e.g., get_prompt for a specific prompt, or get_prompts_by_category for listing all in a category). No when-not-to-use guidance is provided.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Tool Schema Changelog
Recent tool additions, removals, and schema changes observed during successful MCP inspections. Dates show when Glama detected each change.
4 tool updates
- First observed
get_prompt - First observed
get_prompts_by_category - First observed
list_categories - First observed
search_prompts
Frequently Asked Questions
Claiming proves that you control a remote MCP connector. It does not move, proxy, or interrupt the server.
Open the connector listing, choose Claim ownership, and sign in to Glama.
Complete one verification method:
GitHub identity — fastest for official registry listings. For a namespace such as
io.github.alice/server, link the matching GitHub user, then choose Claim with GitHub. An organization namespace such asio.github.acme/serveralso needs that organization to have installed the Glama AI GitHub App and approved its permissions, because GitHub discloses organization membership only to apps it has installed. Use HTTP or DNS when it has not.HTTP challenge — works when you can deploy a public file. Generate a token, publish the exact JSON Glama shows at
/.well-known/glama.jsonon the same origin as the connector, then choose Check HTTP challenge.DNS challenge — works when you control DNS but cannot change the server. Generate a token, create the exact TXT record Glama shows, wait for it to propagate, then choose Check DNS challenge.
After verification, Glama sends a confirmation email and gives you access to listing details, thumbnails, health checks, and analytics. Keep the HTTP file or DNS record in place: Glama periodically checks it and ownership remains verified while the token is discoverable.
The HTTP ownership file has this structure:
{
"$schema": "https://glama.ai/mcp/schemas/connector.json",
"claim": "glama_claim_..."
}Claim tokens are opaque, stable, and bound to the signed-in Glama account. They contain no email address or other personal information. If Glama can no longer discover a verified HTTP or DNS token, it starts a seven-day grace period before removing claim-based access. Restore the same token during that period to keep ownership verified. Never publish an email address, Glama session token, GitHub token, or connector credential as ownership proof.
If verification fails, confirm that you copied the current token exactly. The HTTP file must be public, return valid JSON with a successful HTTP response, and stay on the connector's origin. DNS changes may need more time to propagate. A claim cannot transfer to a different origin or hostname: if the connector target changes, Glama starts the grace period and the new target must be claimed separately after the previous claim is released.
For a connector linked to the official MCP Registry, registry updates continue to replace its name, description, and URL by default. After claiming, open Manage connector and enable Use Glama listing details as the source of truth if edits made on Glama should be preserved. Categories and thumbnails are always managed on Glama; registry linkage and technical connection settings continue to sync.
Control your server's listing on Glama, including description and metadata
Access analytics and receive server usage reports
Get monitoring and health status updates for your server
Feature your server to boost visibility and reach more users
To improve your MCP server's ranking:
Claim ownership of the server listing
Complete the server profile with an accurate description and thumbnail
Provide a test profile so Glama can connect to and evaluate the server
Keep tool definitions clear and complete to earn a high Tool Definition Quality Score (TDQS)
Route real usage through the Glama Gateway; more recorded successful server uses also improve the ranking
For users:
Full audit trail – every tool call is logged with inputs and outputs for compliance and debugging
Granular tool control – enable or disable individual tools per connector to limit what your AI agents can do
Centralized credential management – store and rotate API keys and OAuth tokens in one place
Change alerts – get notified when a connector changes its schema, adds or removes tools, or updates tool definitions, so nothing breaks silently
For server owners:
Proven adoption – public usage metrics on your listing show real-world traction and build trust with prospective users
Tool-level analytics – see which tools are being used most, helping you prioritize development and documentation
Direct user feedback – users can report issues and suggest improvements through the listing, giving you a channel you would not have otherwise
The connector status is unhealthy when Glama is unable to successfully connect to the server. This can happen for several reasons:
The server is experiencing an outage
The URL of the server is wrong
Credentials required to access the server are missing or invalid
If you are the owner of this MCP connector and would like to make modifications to the listing, including providing test credentials for accessing the server, please contact support@glama.ai.
Discussions
No comments yet. Be the first to start the discussion!
Related MCP Connectors
Trade Robinhood through natural language in Claude Code.
90+ free tools, Claude & ChatGPT: prices, options, SEC filings, 13F, insider, congress, transcripts.
Trade across 22+ exchanges and brokers from any MCP-capable AI agent, no install required.
100+ MCP tools for AI agents: content metadata, trade intelligence, business-expertise analysis.
Related MCP Servers
- AlicenseNot gradedqualityDmaintenanceThe most complete AI-powered trading toolkit for Claude and MCP clients, offering backtesting, live sentiment, Yahoo Finance, and 30+ technical analysis tools in one MCP server.MIT
- AlicenseNot gradedqualityDmaintenanceConnect your Robinhood account to Claude Code (or any MCP client) for conversational portfolio management and automated trading.1MIT
- FlicenseNot gradedqualityBmaintenanceA local-first MCP server that connects Claude Code to your Robinhood account, enabling read-only portfolio insights, tax analysis, and natural language queries, with optional human-in-the-loop trading actions.-
- AlicenseBqualityBmaintenanceAI-powered trading toolkit with backtesting, live sentiment, Yahoo Finance data, and 30+ technical analysis tools, integrated as an MCP server for Claude and other AI clients.374,309MIT
Glama MCP Gateway
Add one secure layer between your agents and this server.
TDQS
Each tool has a distinct purpose: retrieving a single prompt by ID, fetching prompts by category, listing categories, and searching prompts. No functional overlap.
All tool names follow a consistent verb_noun pattern in snake_case (get_prompt, get_prompts_by_category, list_categories, search_prompts). Predictable and clear.
4 tools is well-scoped for a prompt library server. Each tool covers a core operation without redundancy, and the count feels neither too sparse nor overwhelming.
The tool set covers retrieval and search comprehensively, but lacks create/update/delete operations. Given the server's likely read-only purpose, this is a minor gap.