SINTEGRA: RS
Server Details
SINTEGRA: RS, official-source lookup. Platform-hosted, pay per query with prepaid credit.
- Status
- Healthy
- Last Tested
- Transport
- Streamable HTTP
- URL
- Repository
- mcp-dir/sintegra_rs-mcp
- GitHub Stars
- 0
- Server Listing
- SINTEGRA RS MCP Server
Available Tools
7 toolsauthenticateAIdempotentInspect
MCP.AI for IDE agents (Cursor, etc.): log in in the browser, copy the access token. Best: add it to this server's config as a header Authorization: Bearer <token> for a permanent, non-expiring connection. Or paste it here for a session-only login: call with { token: "" } after the user pastes, or with no args to get the link.
| Name | Required | Description | Default |
|---|---|---|---|
| token | No |
TDQS
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
The description goes beyond annotations (which already indicate non-read-only, non-destructive, idempotent) by explaining the login flow, the distinction between permanent and session-only logins, and the behavior when called with or without arguments. It does not explicitly mention side effects like modifying server config, but it alludes to adding the token to config, and there is 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 information-dense and relatively concise, packing the login funnel, token handling, and both invocation modes into two sentences. It is slightly run-on and could be cleaner, but every clause serves a purpose and the structure is front-loaded with the tool's core 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?
For an authentication tool with no output schema, the description covers the essential workflow: how to log in, how to provide the token, and the no-args behavior. It lacks explicit detail about the return value (though 'get the link' is mentioned) and does not mention error cases, but the information provided is sufficient for an agent to invoke the tool correctly in most contexts.
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?
Although the schema has only a single optional 'token' parameter with 0% description coverage, the description fully explains its semantics: it is a JWT to be pasted for session-only login. It also clarifies the behavior of calling with no args (returns the login link). This fully compensates for the schema's lack of description.
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 identifies the tool's purpose: authenticating to MCP.AI for IDE agents. It uses the specific verb 'log in' and describes the resource (MCP.AI for IDE agents), and its distinction from sibling tools (e.g., connect, show_version) is implicit but sufficient.
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 usage guidance: the preferred method (adding the token as a header in the server config) and an alternative (session-only login via pasting the token). It explains when to use each approach, which is more than a vague 'use this tool for X.' It does not explicitly compare to sibling tools, but the internal alternatives are clear.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
connectARead-onlyIdempotentInspect
Returns connection status and URLs. When all providers are connected, returns authenticated:true and empty pending[]. When credentials are missing, returns connect_url for the toolkit and per-install URLs.
| Name | Required | Description | Default |
|---|---|---|---|
No parameters | |||
TDQS
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare read-only, idempotent, and non-destructive behavior. The description adds useful conditional context: authenticated state vs. missing credentials and the corresponding URLs. It does not contradict the annotations and provides meaningful behavioral detail beyond the structured metadata.
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, immediately states the core purpose, and then provides the two relevant outcome states without any filler. Every sentence contributes useful 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?
There is no output schema, but the description covers the two return states and the URL fields the caller can expect. It does not specify the exact structure of per-install URLs, but for a simple status-check tool with zero parameters and clear annotations, this is adequate.
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 there is no parameter documentation burden. The 100% schema coverage and absence of required inputs are already clear, and the description appropriately focuses on output behavior instead.
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 ('Returns connection status and URLs') and explains conditional return behavior, clearly distinguishing this status-check tool from siblings like authenticate. The two state-based outcomes further clarify its 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 gives clear context on when the tool is useful and what results to expect in different states, but it does not explicitly contrast it with alternatives such as authenticate or provide explicit 'use when' language. The behavioral context is strong enough for an agent to infer the right usage.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
marketplaceAInspect
The official mcp.ai marketplace — the in-platform catalog of every MCP/tool, AND the way to run them. Covers capability requests like "find an MCP that does X", "consulta um CPF", "is there a tool for Y". Core flow: action=search discovers MCPs by intent → describe returns one MCP's full profile (every tool with its id + params, pricing, auth) so you pick the right tool_id → invoke RUNS that tool. KEY: invoke works even when the MCP is NOT installed — it runs the tool pontualmente (one-off), without adding the MCP to the toolkit and without bloating the tool list. If the MCP needs a credential/login, invoke returns a connect link; if it is paid and the wallet is empty, invoke returns a checkout/top-up link (the user opens it, then you retry). Use install only to make an MCP PERMANENT in the active toolkit (its tools then show up natively in future sessions); prefer invoke for a single/occasional use. list_tools lists what is callable right now. subscribe/cancel handle per-MCP billing; report_bug sends feedback; request_mcp asks us to build a NEW MCP when nothing fits. Search/describe flag installed_in_toolkit vs installed_in_workspace. Writes (install/uninstall/subscribe/cancel and the one-off install behind invoke) require workspace owner/admin. It also carries the mcp.ai PROMPT LIBRARY, which is about ready-made prompt TEXT rather than MCPs: search_prompts finds one, get_prompt returns its full text with {{variables}} filled, and publish_prompt saves a prompt and returns a shareable mcp.ai/p/ link that opens without login.
| Name | Required | Description | Default |
|---|---|---|---|
| limit | No | ||
| query | No | ||
| action | No | search | |
| mcp_id | No | ||
| message | No | ||
| tool_id | No | ||
| arguments | No | {} | |
| immediate | No | ||
| tier_slug | No | ||
| prompt_body | No | ||
| prompt_slug | No | ||
| prompt_tool | No | ||
| prompt_vars | No | {} | |
| conversation | No | [] | |
| prompt_title | No | ||
| request_name | No | ||
| cancel_reason | No | ||
| cancel_comment | No | ||
| prompt_targets | No | ||
| report_context | No | ||
| prompt_category | No | ||
| request_details | No | ||
| prompt_description | No |
TDQS
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Despite the annotations (readOnlyHint=false, openWorldHint=true) being minimal, the description discloses the impermanent one-off semantics of invoke ('runs the tool pontualmente... without adding the MCP to the toolkit'), the conditional auth/payment failure modes, and the owner/admin permission gate for writes. No contradiction with annotations; the description carries its weight and more.
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?
Information density is high, but the prose is a wall of long em-dashed run-ons ('DESCRIBE returns one MCP's full profile (every tool with its id + params, pricing, auth)') crammed into 5 huge sentences. The prompt-library section is buried at the very end after a paragraph break like an afterthought. This text needs bullets or flow markers; even a period instead of an em-dash would help.
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 tool this complex (14 actions, 23 params, no output schema), the description is remarkably thorough. It covers end-to-end flow, error/link cases, permission requirements, the prompt library sub-domain, and even hints at output shape for describe. Nothing lost by the lack of output schema since the prose explicitly names what gets returned.
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 0% and the description is the sole carrier of param semantics. It does document the core action flow well (tool_id, arguments, prompt_vars via '{{variables}} filled'), but with 23 params, several remain obscure: cancel_reason's 8-value enum, tier_slug, immediate, conversation, prompt_targets, and the distinction between message/report_context/request_details are never clarified.
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?
Instantly establishes itself as "The official mcp.ai marketplace — the in-platform catalog... AND the way to run them," giving a concrete noun-verb-object with clear scope. The core search→describe→invoke flow is spelled out, and the prompt library sub-domain is also covered, making the tool fully distinguishable from siblings. There is zero ambiguity about what 'marketplace' does that no other tool does.
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 when/when-not guidance abounds: 'prefer invoke for a single/occasional use' vs 'Use install only to make an MCP PERMANENT in the active toolkit.' It also tells the agent what callback links to expect (connect link, checkout link) and to retry after checkout. The capability-intent mapping at the start ('find an MCP that does X') is exemplary for routing behavior.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
report_bugAIdempotentInspect
Report a bug, missing feature, or send feedback. Include the conversation array with recent messages for reproduction.
| Name | Required | Description | Default |
|---|---|---|---|
| context | No | ||
| message | Yes | ||
| conversation | No | [] |
TDQS
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already convey idempotentHint=true and readOnlyHint=false, so the safety profile is known. The description adds the behavioral requirement to include recent conversation messages for reproduction, but it doesn't disclose what happens after reporting (e.g., whether a response is returned, or if data is sent externally). This is acceptable 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 a single, front-loaded sentence that states the purpose and a key usage hint. Every word earns its place, with no fluff or repetition of schema data.
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?
This is a simple tool with no output schema and no nested objects. The description covers the main purpose and the most important parameter guidance. However, it omits how to handle the 'context' parameter and what the user should expect after submitting, but for a bug-report tool this is mostly adequate.
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 0%, so the description must compensate for parameter meanings. It only mentions the conversation array, without explaining the required 'message' field, the 'context' field, or that conversation is a string (likely JSON) with a default of '[]'. This leaves significant ambiguity.
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 with a specific verb and resource: 'Report a bug, missing feature, or send feedback.' This distinguishes it from unrelated sibling tools like authenticate, connect, and marketplace.
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 context that the conversation array should be included for reproduction, which helps the agent know what to provide. It does not explicitly name alternatives or when-not-to-use, but the sibling tools are clearly different domains, so the usage context is clear enough.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
show_versionARead-onlyIdempotentInspect
Show the current MCP platform and adapter versions.
| Name | Required | Description | Default |
|---|---|---|---|
No parameters | |||
TDQS
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations thoroughly cover the behavioral profile with readOnlyHint=true, idempotentHint=true, and destructiveHint=false. The description adds no extra information beyond the purpose, but given the trivial read-only nature, annotations are sufficient. No contradiction exists.
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, concise sentence that fully conveys the tool's function. It is front-loaded with the action and object, with no unnecessary filler 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?
For a simple informational tool with no parameters and no output schema, the description is complete. It specifies exactly what versions are shown (MCP platform and adapter) and needs no further elaboration. Sibling tools are distinct, so confusion is unlikely.
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 per the rubric baseline is 4. There is no need for parameter documentation, and the schema coverage is 100% (vacuously). The description is consistent.
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: 'Show the current MCP platform and adapter versions.' It uses a specific verb ('Show') and resource ('versions'), and it distinguishes from sibling tools like authenticate, connect, and report_bug, which serve entirely different functions.
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 context but does not explicitly state when to use it versus alternatives. Since the tool is for informational version checking and siblings are unrelated, the intended use is clear, but no explicit when/when-not guidance is provided.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
sintegra_rs_consultarARead-onlyIdempotentInspect
SINTEGRA: RS, consulta em fonte oficial. Hospedado pela plataforma, sem credenciais da plataforma, pague por consulta com crédito pré-pago. Consulta informação de fontes e órgãos oficiais brasileiros (a mesma disponível ao cidadão), não é dado sigiloso. O cliente é o controlador dos dados e responde pela finalidade legítima (LGPD).
| Name | Required | Description | Default |
|---|---|---|---|
| ie | No | ||
| cnpj | No |
TDQS
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Beyond the annotations (readOnly, idempotent, destructive), the description discloses that there is a per-query cost ('pague por consulta com crédito pré-pago'), no platform credentials required ('sem credenciais da plataforma'), and that the data is not confidential ('não é dado sigiloso'). These add significant operational context not captured by 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 front-loaded with the primary purpose and each sentence adds a distinct piece of information (purpose, hosting/payment, data nature, legal responsibility). There is no redundancy or filler, making it appropriately sized for the tool's complexity.
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?
While the description covers service context (hosting, auth, cost, legal), it fails to explain how to invoke the tool correctly: it does not describe the return value, nor does it clarify the 'ie' and 'cnpj' parameters. Given no output schema, this is a significant gap for an agent to call the tool 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?
The input schema has 0% description coverage, and the tool description never mentions the 'ie' or 'cnpj' parameters or their meanings. The agent is left without any guidance on what values to provide or whether one is required, making it a minimal score.
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 does a SINTEGRA query for Rio Grande do Sul ('SINTEGRA: RS, consulta em fonte oficial'), with a specific action verb ('consulta') and resource (official SINTEGRA RS source). It also differentiates from sibling tools by the 'RS' qualifier, even though no explicit alternative is mentioned.
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 useful usage context: hosted by the platform, no platform credentials needed, prepaid credit required, and a legal condition (client as data controller per LGPD). However, it does not explicitly state when not to use the tool or name alternatives, so it falls short of a 5.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
toolkit_infoARead-onlyIdempotentInspect
Returns the current toolkit state: installed MCPs, their connection status, the accounts connected to each one, and how many catalog tools each exposes.
| Name | Required | Description | Default |
|---|---|---|---|
No parameters | |||
TDQS
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint, idempotentHint, and destructiveHint=false, so the behavior is well-covered. The description adds value by specifying what data is returned: installed MCPs, connection status, accounts, and catalog tool counts. Since it's a read-only introspection tool, additional behavioral context like rate limits is less critical.
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 one sentence, compact, and information-dense. It lists exactly what the tool returns, making every word count. No fluff or redundancy.
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 with no parameters and a clear output structure. The description covers the essentials, but could mention whether the connection status is real-time or cached, and whether the account list is filtered. However, given the simplicity nhẹ and the lack of output schema, the description is adequate.
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 parametersaineist, and the schema coverage is 100% (empty properties). The description clearly explains what information is returned, which compensates for the lack of parameters. With no params, the baseline is 4, and the description exceeds it by detailing the output structure.
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 what the tool does: returns the current toolkit state with specific details about installed MCPs, connection status, accounts, and catalog tool counts. This distinguishes it from siblings like connect and authenticate which are actions.
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 that the tool is used to get an overview of the toolkit state, which is distinct from performing actions like connect or authenticate. It doesn't explicitly mention when not to use it, but the context is clear enough for an agent to decide between introspective and action-oriented tools.
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.
No tool schema history has been recorded yet.
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
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Glama MCP Gateway
Add one secure layer between your agents and this server.
TDQS
Most tools have clearly distinct purposes (authentication, connection status, marketplace, version, consultation). However, connect and toolkit_info both return connection-related status, creating slight overlap. Marketplace is a mega-tool with many sub-actions, but its description is thorough enough to avoid confusion.
Naming conventions are inconsistent: some tools are verbs (authenticate, connect, report_bug), some are nouns (marketplace, toolkit_info), and one is a Portuguese compound verb (sintegra_rs_consultar). There is no consistent verb_noun pattern, and the language mix further weakens predictability.
With 7 tools, the set is well-scoped for a platform management server. Each tool serves a distinct high-level function, and the count is within the ideal range without feeling bloated or sparse.
The tool surface covers core platform operations: authentication, connection status, marketplace interactions (search/invoke/install), bug reporting, version display, and toolkit state. Minor gaps exist, such as no explicit disconnect or dedicated credential management beyond what marketplace/connect offer, but agents can work around these.