Click on "Install Server".
Wait a few minutes for the server to deploy. Once ready, it will show a "Started" state.
In the chat, type @ followed by the MCP server name and your instructions, e.g., "@api-mind list available APIs"
That's it! The server will respond to your query, and you can continue using it as needed.
Here is a step-by-step guide with screenshots.
MCP server for API discovery from .mind spec files. Works with Claude Code, Claude Desktop, and any MCP-compatible AI assistant.
# 1. Install spec-mind to generate .mind files from OpenAPI specs
brew install spec-mind
# 2. Run the setup script from your project directory
curl -fsSL https://raw.githubusercontent.com/msegoviadev/api-mind-mcp/main/setup-mcp.sh | bash
# 3. Add your OpenAPI specs and generate .mind files
mkdir specs
cp your-api.yaml specs/
spec-mind sync --no-notation ./specs/
# 4. Restart Claude CodeIn Claude Code:
What APIs are available?
Show me the endpoints for payments
Call GET /payments/{id} in devRelated MCP server: apifable
Install spec-mind to generate .mind files from your OpenAPI specs:
brew install spec-mindRun setup-mcp.sh from your project directory:
curl -fsSL https://raw.githubusercontent.com/msegoviadev/api-mind-mcp/main/setup-mcp.sh | bashOr for user-wide installation (all projects):
curl -fsSL https://raw.githubusercontent.com/msegoviadev/api-mind-mcp/main/setup-mcp.sh | bash -s -- --globalThe script:
Creates a specs/ directory in your project
Registers api-mind in Claude Code with the correct specs path
Scaffolds ~/.config/api-mind/dev.env for environment defaults
# Copy your OpenAPI/Swagger YAML/JSON files to specs/
cp your-api.yaml specs/spec-mind sync --no-notation ./specs/User: "What APIs are available?"
Claude: *uses list_apis tool*
"Found 2 APIs: ecommerce, payments"
User: "Show me payment endpoints"
Claude: *uses list_endpoints tool*
"POST /payments [auth: oauth2]
GET /payments/{id}"
User: "Call GET /payments/{id} in dev"
Claude: *uses get_endpoint_schema + get_call_context tools*
"Resolved base URL: https://api.dev.example.com
Calling GET /payments/123..."Lists all APIs loaded from the specs folder.
Input: none
Output: JSON with API names, titles, base URLs, and environmentsLists endpoints across all APIs.
Input:
filter (optional): Substring match on method, path, or section
Output: JSON with environments and endpoint listReturns full context for an endpoint.
Input:
api: API name
method: HTTP method
path: Endpoint path
Output: Text block with base URL, environments, auth, and schemaCall before constructing curl to understand the endpoint contract.
Returns runtime context needed to execute API calls.
Input:
api: API name
env (optional): Environment to use (dev, stage, uat). Defaults to dev.
Output: Resolved base URL, active environment, and default values for credentials and parametersCall this before constructing curl when the user wants to actually invoke an endpoint.
Reads from ~/.config/api-mind/<env>.env and ~/.config/api-mind/<api>/<env>.env.
When get_endpoint_schema shows auth requirements, construct headers:
Auth in Schema | curl Header |
| No header |
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get_call_context reads default values from ~/.config/api-mind/:
~/.config/api-mind/
dev.env # base defaults for all APIs (dev environment)
stage.env # base defaults for stage
auth0/
dev.env # API-specific overrides for auth0Each .env file uses key=value format (lines starting with # are ignored). The base_url key overrides the placeholder URL from the spec.
Example ~/.config/api-mind/dev.env:
base_url=https://api.dev.example.com
auth0_client_id=abc123
auth0_cacert=/etc/ssl/cert.pemsetup-mcp.sh scaffolds this file on first run.
list_apis → list_endpoints → get_endpoint_schema → get_call_context → [LLM constructs curl] → bashlist_apis - Discover available APIs
list_endpoints - Find relevant endpoints
get_endpoint_schema - Get endpoint contract (URL, auth, schema)
get_call_context - Resolve base URL and credentials for the target environment
LLM constructs curl command using resolved values
LLM executes via bash tool
claude mcp add --transport stdio api-mind \
--env SPECS_DIR=/absolute/path/to/specs \
-- npx -y @msegoviadev/api-mind-mcpImportant: Always use absolute paths. MCP servers run as standalone processes without project context.
Create .mcp.json in your project root:
{
"mcpServers": {
"api-mind": {
"command": "npx",
"args": ["-y", "@msegoviadev/api-mind-mcp"],
"env": {
"SPECS_DIR": "/absolute/path/to/specs"
}
}
}
}Note: Each team member needs their own .mcp.json with their absolute path. Add .mcp.json to .gitignore.
git clone https://github.com/msegoviadev/api-mind-mcp
cd api-mind-mcp
npm install
npm run build
# Test locally
node dist/index.js /path/to/specsMIT
Returns the runtime context needed to execute API calls: resolved base URL, active environment, and default values for credentials and parameters.
Call this before constructing a curl command when the user wants to actually invoke an endpoint. Do NOT call this just to browse or understand the API shape — use list_endpoints and get_endpoint_schema for that.
Reads from ~/.config/api-mind/.env and ~/.config/api-mind//.env. Defaults to "dev" unless the user specifies otherwise.
| Name | Required | Description | Default |
|---|---|---|---|
| api | Yes | API name matching the .mind filename (e.g. auth0-management-v2) | |
| env | No | Environment to use (e.g. stage, uat). Defaults to dev. |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries the full burden. It discloses the source files (~/.config/api-mind/...) and the default environment ('Defaults to dev'). It implies a read-only operation by describing a 'return' of context, but doesn't explicitly declare non-mutation. Still, it adds meaningful behavioral context beyond a bare description.
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?
Three sentences, each serving a distinct purpose: what it does, when to use it, and where it reads from. No redundancy, front-loaded purpose, and well-structured.
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 is simple (2 params, no output schema). The description covers purpose, usage, and behavior. It doesn't detail error scenarios or exact return format, but given the simplicity and 100% schema coverage, it is reasonably complete. Could be slightly more explicit about what happens if files are missing, but not a major gap.
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 coverage is 100%, with descriptions for both 'api' and 'env'. The description reiterates the default for env ('Defaults to dev') but doesn't add significant new meaning beyond what the schema already provides. The baseline for high schema coverage is 3, and no extra params are explained.
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 purpose: 'Returns the runtime context needed to execute API calls' and specifies the output components (base URL, environment, credentials/parameter defaults). It also differentiates from siblings by explicitly contrasting with list_endpoints and get_endpoint_schema.
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?
Explicit guidance is provided: 'Call this before constructing a curl command when the user wants to actually invoke an endpoint.' It also gives a clear exclusion: 'Do NOT call this just to browse or understand the API shape — use list_endpoints and get_endpoint_schema for that.' This fully addresses when and when not to use the tool.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Returns the full context for a specific endpoint including resolved URL, auth requirements, and schema.
Use this to understand an endpoint before constructing a curl command to execute via the bash tool. Call this after list_endpoints to get the endpoint contract.
Auth in Schema | curl Header |
| No header |
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? optional | [ro] readOnly | [w] writeOnly | =val default
^ header | ~ cookie | *N multipleOf N | | enum or nullable
OneOf<A,B> on field = discriminated union
{*:T} = map/dict | {...} = open object | extends = allOf
& = inline extension | ~~name~~ deprecated | # = inline note
[multipart] [form] [binary] [text] = request body encoding
| Name | Required | Description | Default |
|---|---|---|---|
| api | Yes | API name matching the .mind filename | |
| path | Yes | Endpoint path | |
| method | Yes | HTTP method (GET, POST, PUT, PATCH, DELETE) |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the full burden. It adds valuable behavioral context beyond the bare purpose: an Auth Patterns table explaining how schema auth values map to curl headers, and a detailed NOTATION legend for interpreting the returned schema. It stops short of explicitly stating read-only semantics, but the get-oriented purpose makes side effects unlikely.
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 front-loaded with purpose and usage in the first two sentences, then organized into two reference tables. While the notation legend is long, it earns its place by compensating for the absence of an output schema. The table format is scannable and each section has clear 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?
For a moderate-complexity lookup tool with no output schema and no annotations, the description covers the key gaps: what is returned (resolved URL, auth, schema), how to interpret the returned schema (notation legend), and the end-to-end workflow (after list_endpoints, before bash curl). It lacks error/edge-case behavior but is otherwise 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?
Schema description coverage is 100%, with each parameter (api, path, method) already well-described in the schema. The description adds workflow context ('Call this after list_endpoints') but no additional parameter-level semantics, so the high-coverage 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 opens with a specific verb+resource: 'Returns the full context for a specific endpoint including resolved URL, auth requirements, and schema.' It distinguishes from siblings by framing it as the post-list_endpoints step for obtaining an endpoint contract, and from get_call_context by focusing on endpoint schema rather than call context.
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?
Explicit guidance is provided: 'Use this to understand an endpoint before constructing a curl command to execute via the bash tool' and 'Call this after list_endpoints to get the endpoint contract.' This names both the predecessor tool (list_endpoints) and the downstream integration (bash/curl), establishing a clear workflow.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Lists all APIs loaded from the specs folder, with their names, titles, base URLs, and available environments.
Use this when you need to know what APIs are loaded or what environments a specific API supports. Call this first when the user references an API you haven't seen yet.
| Name | Required | Description | Default |
|---|---|---|---|
No parameters | |||
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 clearly indicates a read-only listing behavior and specifies the returned data. However, it does not discuss potential limitations (e.g., whether the list is sorted, if it reflects live changes, or any access restrictions), which prevents a higher score.
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 long, front-loaded with the core function and followed by usage guidance. Every sentence earns its place with no redundancy or fluff.
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?
For a parameterless listing tool with no output schema, the description fully covers what the tool does, what it returns, and when to call it. It also provides a prioritization heuristic ('Call this first'), making it comprehensive for the given complexity.
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 tool has zero parameters and the schema is empty, so there is nothing to explain. As per the baseline for 0 params, the description does not need to add parameter semantics. Score 4 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 uses a specific verb and resource: 'Lists all APIs loaded from the specs folder.' It clearly states the scope and return fields (names, titles, base URLs, environments). Though it does not explicitly name sibling tools, the focus on APIs distinguishes it from list_endpoints and get_endpoint_schema.
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 provides explicit guidance: 'Use this when you need to know what APIs are loaded or what environments a specific API supports' and 'Call this first when the user references an API you haven't seen yet.' It lacks mention of alternatives or when not to use, but the context is clear and actionable.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Lists all available endpoints across all loaded .mind files. Also surfaces the available environment names.
Use this to discover what endpoints exist across all APIs. Filter by method, path, or section. Call this when the user references an API or asks what's available.
| Name | Required | Description | Default |
|---|---|---|---|
| filter | No | Substring match on method, path, or section name |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries the burden of behavioral disclosure. It mentions that it lists endpoints 'across all loaded .mind files' and 'surfaces environment names', which adds scope and output context. However, it does not describe return structure, error behavior, or any side effects (though likely a safe read-only operation). This is adequate but not rich.
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 three concise sentences, front-loaded with the main purpose, followed by usage guidance and a clear trigger. Every sentence contributes useful information with no redundancy or fluff.
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?
For a simple tool with one optional parameter and no output schema, the description covers the core aspects: what it does, when to use it, and what the filter does. It does not detail the return value structure, but the high-level output hints ('Lists all available endpoints', 'surfaces environment names') are adequate for a discovery tool. Minor gap: no explicit mention of limits or pagination, but not critical.
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 schema already provides 100% coverage for the 'filter' parameter, describing it as a substring match on method, path, or section. The description repeats this in prose ('Filter by method, path, or section') without adding new meaning. Thus, the schema does the heavy lifting, and the description adds marginal value.
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 explicitly states 'Lists all available endpoints across all loaded .mind files' and 'surfaces the available environment names', giving a specific verb, resource, and scope. This clearly distinguishes it from sibling tools like list_apis (which likely lists APIs at a higher level) and get_endpoint_schema (which fetches a single schema).
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 provides clear usage context: 'Use this to discover what endpoints exist across all APIs' and 'Call this when the user references an API or asks what's available.' It stops short of explicitly naming alternatives or listing when-not-to-use scenarios, but the guidance is sufficient for typical discovery purposes.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Recent tool additions, removals, and schema changes observed during successful MCP inspections. Dates show when Glama detected each change.
v0.4.0get_call_contextget_endpoint_schemalist_apislist_endpointsEach tool has a distinct purpose: list_apis for API overview, list_endpoints for endpoint discovery, get_endpoint_schema for endpoint contract details, and get_call_context for runtime execution details. The descriptions explicitly clarify when to use each, preventing overlap.
All tool names follow a consistent verb_noun pattern (list_* and get_*), with verbs clearly indicating read-only discovery actions. The naming is predictable and uniform.
Four tools is well-scoped for a server focused on API discovery and context provision. Each tool covers a necessary step in the workflow from discovering APIs to preparing API calls, without redundancy.
The tool surface covers the full lifecycle of API exploration: discovering APIs, discovering endpoints, understanding schema/auth, and obtaining runtime context for actual calls. There are no obvious gaps for the server's stated purpose.
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