Apideck Unified API MCP — 330 tools across 200+ SaaS connectors (accounting, CRM, HRIS, ATS).
Scored across 4 tools
Each tool serves a distinct purpose: describe, execute, list scopes, list tools. There is no functional overlap, and an agent can easily select the right tool based on its name and description.
Tool names follow a verb_noun pattern with lower snake_case (e.g., list_tools, execute_tool). 'describe_tool_input' slightly deviates by combining 'tool' and 'input' as a compound object, but the overall pattern remains consistent.
With 4 tools, the server is well-scoped for its purpose as an MCP management interface, providing discovery, description, execution, and scope listing without unnecessary bloat.
The set covers the core operations for working with MCP tools: discovering tools, inspecting input schemas, executing them, and checking scopes. A minor gap is the lack of a dedicated tool for retrieving full tool metadata (beyond input schema), but this is workable.
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. |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint=true and idempotentHint=true, indicating this is a safe read operation. The description adds little behavioral context beyond the purpose itself; it does not mention error cases (e.g., unknown tool name) or the exact format of the returned schema. 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.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, focused sentence stating the exact operation. No filler or repetition; every word contributes to the meaning.
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 one-parameter introspection tool with strong annotations, the description sufficiently covers its return value. However, it lacks any note about behavior when the tool name is invalid, which might be useful but is not critical given the tool's simplicity.
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 a descriptive parameter entry for 'name' including an example and source ('as returned by list_tools'). The description itself adds no parameter-specific meaning, so it relies on the schema, which is adequate.
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 a specific verb 'Return' and a well-defined resource: 'the JSON-Schema input contract for a tool by name.' This is unambiguous and distinguishes it from siblings like execute_tool or list_tools.
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. Siblings are listed but not compared, and there is no mention of prerequisites or typical workflows (e.g., 'use before calling execute_tool'). The implied use case is not stated.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
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. |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
The description discloses that input is 'forwarded raw,' which is a behavioral detail beyond what annotations alone convey (e.g., openWorldHint, readOnlyHint). This tells agents that arguments are not preprocessed, which is essential for constructing valid input. No contradictions with annotations exist.
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-loaded with the core purpose and free of redundant information. Every word earns its place, and the structure is clean and scannable.
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 a dynamic dispatcher with rich schema descriptions and annotations covering side-effect hints, so the description is largely sufficient. It does not describe the return value or error behavior, but those are inherently dependent on the invoked tool and less critical given the schema's thoroughness. A slightly more explicit note about behavior varying by target tool would improve it.
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% as both parameters have detailed descriptions, so the baseline is 3. The description adds little beyond the schema, as 'forwarded raw' essentially repeats the schema's 'forwarded verbatim' for the input parameter. No additional parameter semantics are provided.
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 function with a specific verb and resource: 'Invoke a tool by name.' This distinguishes it from sibling tools like list_tools or describe_tool_input, which inspect rather than execute. The additional phrase about forwarding input raw further clarifies the tool's role.
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 no guidance on when to use this tool versus alternatives. It does not mention prerequisites, such as looking up the tool name with list_tools or fetching the input schema with describe_tool_input. Those details appear only in the schema property descriptions, not in the main description.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Return the list of allowed MCP scopes.
| Name | Required | Description | Default |
|---|---|---|---|
No parameters | |||
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint=true and destructiveHint=false. The description adds the qualifier 'allowed,' implying scopes may be restricted, but provides no additional behavioral context beyond what annotations already convey.
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: 'Return the list of allowed MCP scopes.' Every word earns its place, with no redundancy or unnecessary detail.
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, parameterless tool, the description sufficiently communicates the return value (a list of scopes). While it lacks details on output format or edge cases, the tool's triviality means this is adequate completeness.
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 0 parameters, and the schema is empty, so there is no parameter meaning to explain. The description is not required to compensate, and the baseline for 0-param tools 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 the specific verb 'Return' and identifies the resource as 'list of allowed MCP scopes,' which clearly distinguishes it from sibling tools like list_tools (which lists tools) and execute_tool (which executes tools).
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 no guidance on when to use this tool versus alternatives. It does not mention any context, prerequisites, or exclusions, leaving the agent to infer usage solely from the tool's name.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
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. |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint=true, destructiveHint=false, and idempotentHint=true, so the safety profile is clear. The description adds the behavioral nuance of the no-args call and the filtering options, providing context beyond the annotations without contradicting them.
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-loaded with the core purpose, and includes essential usage instructions without any redundant words 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 discovery tool with comprehensive schema and annotations, the description covers the main usage patterns (no-arg and filtered). It doesn't describe the return format, but the purpose and filtering behavior are sufficiently clear for an agent to invoke it correctly.
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 schema already explains each parameter in detail. The description merely restates parameter names without adding new semantics, so the baseline of 3 applies.
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 starts with 'Discover Apideck tools,' which clearly states the action and resource. It distinguishes itself from siblings by focusing on discovery/listing rather than execution or input description, and the mention of filtering adds specificity.
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 gives explicit usage modes: call with no args for a domain index, or filter by domain/search_terms/scope. It doesn't explicitly mention alternatives, but the purpose is clear enough that the agent can infer when to use this tool versus siblings like execute_tool or describe_tool_input.
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.
describe_tool_input2 fields changedInput schema / $schemaRemoved value: -"http://json-schema.org/draft-07/schema#"Input schema / properties / name / descriptionAdded value: +"Exact tool name to describe (e.g. \"accounting-invoices-create\"), as returned by list_tools."execute_tool7 fields changedInput schema / $schemaRemoved value: -"http://json-schema.org/draft-07/schema#"Input schema / properties / input / additionalPropertiesAdded value: +{}Input schema / properties / input / descriptionAdded value: +"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."Input schema / properties / input / propertyNamesAdded value: +{
+ "type": "string"
+}Input schema / properties / input / typeAdded value: +"object"Input schema / properties / name / descriptionAdded value: +"Exact tool name to invoke (e.g. \"accounting-invoices-create\"), as returned by list_tools."Input schema / requiredPrevious value: -[
- "name",
- "input"
-]New value: +[
+ "name"
+]list_scopes2 fields changedInput schema / $schemaRemoved value: -"http://json-schema.org/draft-07/schema#"Input schema / requiredAdded value: +[]list_tools5 fields changedInput schema / $schemaRemoved value: -"http://json-schema.org/draft-07/schema#"Input schema / properties / domain / descriptionAdded value: +"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."Input schema / properties / scope / descriptionAdded value: +"Filter by mutation scope: \"read\" (no writes), \"write\" (creates/updates), or \"destructive\" (deletes/overwrites). Omit to include all scopes."Input schema / properties / search_terms / descriptionAdded value: +"Case-insensitive substring terms ANDed across a tool name/description (e.g. [\"invoice\",\"create\"]). Omit to list everything in the domain."Input schema / requiredAdded value: +[]Unified.to MCP server — one API for 500+ B2B SaaS integrations across 28+ categories
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