Apideck MCP
Server Details
Model Context Protocol server for the Apideck Unified API. Connect any MCP-compatible agent framework to 100+ accounting systems, HRIS platforms, file storage providers, and more through one integration. More information https://www.apideck.com/mcp-server
- Status
- Healthy
- Last Tested
- Transport
- Streamable HTTP
- URL
Glama MCP Gateway
Connect through Glama MCP Gateway for full control over tool access and complete visibility into every call.
Full call logging
Every tool call is logged with complete inputs and outputs, so you can debug issues and audit what your agents are doing.
Tool access control
Enable or disable individual tools per connector, so you decide what your agents can and cannot do.
Managed credentials
Glama handles OAuth flows, token storage, and automatic rotation, so credentials never expire on your clients.
Usage analytics
See which tools your agents call, how often, and when, so you can understand usage patterns and catch anomalies.
Tool Definition Quality
Average 3.9/5 across 4 of 4 tools scored.
Each tool serves a distinct purpose: discovering tools, describing inputs, executing tools, and listing scopes. There is no overlap or ambiguity.
All tools follow a consistent verb_noun pattern in snake_case (list_tools, describe_tool_input, execute_tool, list_scopes), making them predictable.
With 4 tools covering discovery, description, execution, and permission scoping, the set is concise and appropriate for a meta-tool server, though slightly minimal.
The tool surface fully covers the required operations for managing and using tools: listing, describing, executing, and checking scopes, with no obvious gaps.
Available Tools
4 toolsdescribe_tool_inputARead-onlyIdempotentInspect
Return the JSON-Schema input contract for a tool by name.
| Name | Required | Description | Default |
|---|---|---|---|
| name | Yes | Exact tool name to describe (e.g. "accounting-invoices-create"), as returned by list_tools. |
Tool Definition Quality
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint=true, idempotentHint=true, and destructiveHint=false. The description does not add behavioral context beyond that, such as rate limits or side effects. It is consistent but does not enhance transparency.
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 of 7 words, perfectly concise and front-loaded. Every word serves a purpose.
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 low complexity (1 parameter, no output schema, no nested objects), the description completely covers what the tool does and how to use it. No information is missing.
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%, so the schema already documents the 'name' parameter. The description mentions 'by name' but does not add new meaning beyond what is in the schema.
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 it returns the JSON-Schema input contract for a tool by name. The verb 'Return' and resource 'JSON-Schema input contract' are specific. It distinguishes itself from siblings like 'list_tools' (which lists tools) and 'execute_tool' (which runs them).
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 when to use (when you need a specific tool's input schema) but does not explicitly state when not to use or provide alternatives. Given the tool's simplicity, this is adequate.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
execute_toolBInspect
Invoke a tool by name. input is forwarded raw to the tool handler.
| Name | Required | Description | Default |
|---|---|---|---|
| name | Yes | Exact tool name to invoke (e.g. "accounting-invoices-create"), as returned by list_tools. | |
| input | No | Arguments object forwarded verbatim to the target tool. Its shape matches that tool's input schema (fetch it with describe_tool_input). Omit for tools that take no arguments. |
Tool Definition Quality
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations indicate readOnlyHint=false, idempotentHint=false, and destructiveHint=false, but the description does not clarify that the actual behavior depends on the target tool. The meta-routing nature is not disclosed, which could mislead an agent about the tool's side effects.
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 extremely concise and front-loaded, stating the core purpose in the first sentence. Every word adds value; there is no unnecessary information.
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 meta nature and lack of output schema, the description should explain that the result is the target tool's output and mention error handling. It is incomplete for an agent to fully understand the invocation lifecycle.
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%, and the description adds minimal new information (e.g., 'input is forwarded raw' is already in the schema). The description does not enhance understanding of parameters beyond the schema.
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 invokes a tool by name, which is distinct from sibling tools (describe_tool_input, list_scopes, list_tools). The verb 'Invoke' and resource 'tool' are specific and unambiguous.
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 explicit guidance on when to use or avoid this tool. While the purpose is self-evident from the name and siblings, there is no mention of prerequisites or alternatives, which could improve usability.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
list_scopesARead-onlyIdempotentInspect
Return the list of allowed MCP scopes.
| Name | Required | Description | Default |
|---|---|---|---|
No parameters | |||
Tool Definition Quality
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint=true, idempotentHint=true, and destructiveHint=false. The description adds minimal behavioral context beyond stating it returns scopes. Adequate but not enhanced.
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?
Single sentence, front-loaded with the core purpose. No unnecessary words or repetition.
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 no parameters, no output schema, and strong annotations covering safety, the description is complete enough. It clearly describes the return value for a simple list tool.
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, so the description does not need to add parameter semantics. Baseline is 4 for no parameters, and the description serves its purpose.
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 'Return' and the resource 'list of allowed MCP scopes'. It differentiates from sibling tools (list_tools, execute_tool, describe_tool_input) by focusing on scopes specifically.
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 is provided on when to use this tool versus alternatives like list_tools. The description only states what it does without contextual usage advice.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
list_toolsARead-onlyIdempotentInspect
Discover Apideck tools. Call with no args for domain index; filter with domain/search_terms/scope.
| Name | Required | Description | Default |
|---|---|---|---|
| scope | No | Filter by mutation scope: "read" (no writes), "write" (creates/updates), or "destructive" (deletes/overwrites). Omit to include all scopes. | |
| domain | No | Restrict results to a single tool domain (e.g. "accounting", "hris"). Omit to get the cross-domain index. Use a key returned by a prior no-arg call. | |
| search_terms | No | Case-insensitive substring terms ANDed across a tool name/description (e.g. ["invoice","create"]). Omit to list everything in the domain. |
Tool Definition Quality
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already indicate readOnlyHint=true and idempotentHint=true. Description adds that it returns a domain index or filtered list, which is minimal additional context beyond annotations.
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?
Two short sentences, front-loaded with purpose, no wasted words. Efficient structure.
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 discovery tool with no required params, the description covers basic usage. Lacks explicit output format, but likely adequate given low 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?
Schema coverage is 100%, baseline 3. Description reiterates parameter roles ('filter with domain/search_terms/scope') but adds no new semantics beyond what the schema provides.
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 'Discover Apideck tools,' specifying the verb and resource. It distinguishes from siblings by listing tools, unlike describe_tool_input, execute_tool, and list_scopes.
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?
Provides explicit guidance: 'Call with no args for domain index; filter with domain/search_terms/scope.' It tells when to use each mode but does not mention when not to use or alternatives.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Claim this connector by publishing a /.well-known/glama.json file on your server's domain with the following structure:
{
"$schema": "https://glama.ai/mcp/schemas/connector.json",
"maintainers": [{ "email": "your-email@example.com" }]
}The email address must match the email associated with your Glama account. Once published, Glama will automatically detect and verify the file within a few minutes.
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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
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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
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