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Self-hosted, open-source MCP gateway: turn any API, database or MCP server into custom connectors for Claude, ChatGPT, Gemini, Copilot & Cursor — no code. Converts REST, SOAP, WSDL, GraphQL & SQL to MCP, with OAuth2, RBAC & audit log. 175+ pre-built adapters. This is the public read-only demo endpoint — run your own at https://github.com/HelpCode-ai/anythingmcp

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Status
Healthy
Uptime
99.1% over 43 days
Last Tested
Transport
Streamable HTTP · MCP 2025-11-25
URL

TDQS

A3.5/5.0

Scored across 4 tools

Disambiguation4/5

The four tools cover distinct informational topics: overview, installation, client connection, and connector listing. There is mild overlap between get_started and connect_client since both describe setup steps, but the descriptions clarify the intended focus.

Naming Consistency4/5

All tools use the consistent 'anythingmcp_' prefix and snake_case names. Three follow a verb_noun pattern (connect_client, get_started, list_connectors), while 'overview' is a noun-only deviation.

Tool Count4/5

Four tools is a reasonable count for a demo/informational MCP server focused on onboarding. It is slightly under what a full connector-management surface would need, but each tool has a clear role in the current scope.

Completeness3/5

The surface covers basic onboarding information but lacks operational tools for actually using, searching, or managing the 265 connectors. For a server named AnythingMCP, agents expecting to interact with connectors would hit notable dead ends.

Available Tools

4 tools
anythingmcp_connect_clientAInspect

Setup instructions to connect an AI client (Claude, ChatGPT, Gemini, Copilot, Cursor) to AnythingMCP.

ParametersJSON Schema
NameRequiredDescriptionDefault
clientYesWhich AI client to connect.

TDQS

A3.8/5.0
Behavior3/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

No annotations are provided. The description says 'Setup instructions,' suggesting an informational output rather than a live connection or mutation, but it does not explicitly state side effects or return format.

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?

One concise sentence with no fluff, and it includes the key enum values, making the tool's purpose immediately understandable.

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?

For a simple instruction-retrieval tool with one enum parameter, the description is sufficient. It could mention what the instructions contain or how it relates to sibling tools, but those details are not essential.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters3/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

The schema already defines the client enum with a clear description. The tool description repeats the client list but adds no additional semantic detail beyond the 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?

Clearly states the tool provides setup instructions for connecting a specific AI client to AnythingMCP, and the enumerated client list distinguishes it from sibling overview/get-started/list tools.

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?

Implies use when the user needs per-client setup instructions, but does not explicitly contrast with get_started, overview, or list_connectors.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

anythingmcp_get_startedAInspect

How to install and run your own AnythingMCP instance with Docker.

ParametersJSON Schema
NameRequiredDescriptionDefault

No parameters

TDQS

A3.6/5.0
Behavior3/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

With no annotations, the description carries the transparency burden. 'How to install and run' suggests an instructional/informational behavior rather than a mutating operation, but it does not state whether the tool returns instructions, starts a process, or has 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.

Conciseness4/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is a single focused sentence with no filler, and the main purpose is front-loaded. It could benefit from clarifying what the returned content looks like, but it is appropriately terse.

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?

For a no-argument getting-started tool, the description supplies the key context (self-hosted setup, Docker). Since there is no output schema, mentioning the output format would make it more complete, but the tool is simple enough that the current description likely suffices.

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 parameters, so there is no parameter documentation burden. The description needs no parameter-level detail.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose4/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description identifies a specific action and resource: installing and running one's own AnythingMCP instance via Docker. It goes beyond a tautology, though it does not explicitly contrast with sibling tools.

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 phrase 'your own AnythingMCP instance' implies the tool is for self-hosting/setup use cases, but there is no explicit when-to-use guidance or mention of alternatives such as connect_client or overview.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

anythingmcp_list_connectorsBInspect

Overview of the 265 pre-built connectors and the connector types you can build.

ParametersJSON Schema
NameRequiredDescriptionDefault

No parameters

TDQS

B3.2/5.0
Behavior2/5

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 implies a read-only listing but does not state that it is non-mutating, does not mention pagination, filtering, or the shape/volume of the returned catalog (265 entries). For an unannotated tool this is thin.

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?

A single front-loaded sentence that names the resource and its scope with no wasted words. Appropriately sized for a zero-parameter listing tool.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness3/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

For a zero-param tool with no output schema, the description should characterize the return value more than 'overview'. It conveys the subject matter (connectors and buildable types) but not format or how to consume the result, leaving a modest gap.

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 takes zero parameters, so the schema has nothing to document and no semantic gap exists. The baseline of 4 applies; nothing in the description misleads about inputs.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose4/5

Does the description clearly state what the tool does and how it differs from similar tools?

States a clear resource (the 265 pre-built connectors) and adds scope detail (connector types you can build). An agent can tell this is a catalog/listing tool. However, it does not differentiate itself from siblings like anythingmcp_overview or anythingmcp_get_started, which sound similarly orientation-oriented.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines2/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

There is no explicit when-to-use or when-not-to-use guidance. A reader can infer it is for discovering available connectors, but nothing distinguishes it from the overview/get_started siblings or states preconditions.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

anythingmcp_overviewAInspect

What AnythingMCP is, what this demo endpoint does, and where to learn more.

ParametersJSON Schema
NameRequiredDescriptionDefault

No parameters

TDQS

A3.5/5.0
Behavior2/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

No annotations are present, so the description bears the responsibility for behavioral disclosure. It does not state whether the tool is read-only, whether it fetches remote data, or if it has any side effects. While 'overview' implies a non-mutating operation, the lack of explicit transparency leaves the agent without clear expectations.

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 a single, well-structured sentence that front-loads the main topic (AnythingMCP overview) and briefly covers the other key points (demo endpoint, learning resources). It is concise, clear, and contains no unnecessary words.

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?

For a simple overview tool, the description is sufficiently complete: it tells the agent what the overview contains (what AnythingMCP is, what the demo endpoint does, where to learn more). It does not specify the output format, but given that no output schema is defined and the tool's scope is introductory, this is not a significant gap.

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 is empty (0 parameters), so the description does not need to explain parameter semantics. According to the rubric, 0 parameters yields a baseline of 4. The description adds no parameter-related information, but none is required.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose4/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states that the tool provides an overview of AnythingMCP, the demo endpoint, and learning resources. It names the specific resource and implies the action of giving an overview, distinguishing it from sibling tools like connect_client or list_connectors. However, it lacks an explicit verb like 'returns' or 'provides', making it slightly less immediate in conveying the action.

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 implies usage by stating what the overview covers, but it does not explicitly say when to use this tool versus the alternatives (e.g., get_started, list_connectors). There is no mention of when not to use it or how it relates to other tools, leaving the selection rationale to the agent's inference.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

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