Enel RJ: Download + OCR
Server Details
Enel RJ: Download + OCR, official-source lookup. Platform-hosted, pay per query with prepaid credit.
- Status
- Healthy
- Last Tested
- Transport
- Streamable HTTP
- URL
- Repository
- mcp-dir/contas_enel_rj_download_ocr-mcp
- GitHub Stars
- 0
- Server Listing
- Enel RJ: Download + OCR
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?
It discloses important behavior beyond annotations: browser login, copying an access token, session-only vs permanent config-based authorization, the use of `Authorization: Bearer`, and the no-args behavior that returns a login link. This aligns with the idempotentHint=annotation and gives agents useful expectations about side effects and persistence.
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 compact and dense with useful information: the authentication flow, config vs session option, and invocation guidance. It's somewhat long and run-on, but every part contributes value and it front-loads the main 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 a single optional parameter, no output schema, and the listed annotations, this description covers the workflows well. It details the practical options and the login sequence, although it doesn't specify what the tool returns on success, failure, or when the token is invalid.
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%, but the description compensates by explaining what the `token` parameter should contain (a pasted JWT) and that omitting the parameter means 'get the login link'. It does not give token format details or describe any other semantics, but the single optional parameter is well contextualized.
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 an authentication/login operation: 'log in... copy the access token' and gives specific invocation modes, including both token-paste and no-argument link retrieval. It distinguishes itself from the sibling tools by describing the IDE auth workflow rather than a generic connect or toolkit action.
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 use-case context: prefer adding the token to config for a permanent connection, or use the session-only token call after the user pastes a JWT, or call with no args to retrieve a login link. It explains the main modes clearly, though it doesn't explicitly describe when not to use this tool relative to sibling tools like 'connect'.
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 readOnlyHint, idempotentHint, and destructiveHint, and the description adds meaningful conditional behavior: when all providers are connected it returns authenticated:true with empty pending[], and when credentials are missing it returns connect_url for the toolkit and per-install URLs. This goes beyond annotation defaults 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?
Two sentences, front-loaded with the core purpose, followed by concise conditional behavior. Every sentence adds value and there is no filler 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?
For a zero-parameter read-only status tool with rich annotations, the description fully explains the important return conditions and URL behavior. No output schema is present, but the description sufficiently covers expected return content for the main scenarios.
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 detail for the description to add. The baseline of 4 is appropriate, and the description's focus on return states compensates for any lack of parameter semantics.
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 names a specific verb+resource ('Returns connection status and URLs') and clearly differentiates itself from the sibling authenticate tool by focusing on status reporting rather than performing authentication. It also provides concrete outcome states, making its purpose unmistakable.
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 the tool is used to check connection/provider status, but it never explicitly states when to use it versus the sibling authenticate tool or any other alternative. The conditional statements describe output behavior, not user decision guidance.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
contas_enel_rj_download_ocr_consultarBRead-onlyIdempotentInspect
Enel RJ: Download + OCR, 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 |
|---|---|---|---|
| empresa | No | ||
| instalacao | No | ||
| login_email | Yes | ||
| login_senha | Yes |
TDQS
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already indicate read-only, idempotent, non-destructive. The description adds meaningful behavioral context: 'pague por consulta com crédito pré-pago' (pay per query with prepaid credit), 'sem credenciais da plataforma' (no platform credentials), and LGPD data controller responsibility. These are beyond annotations and are critical for the agent to set expectations.
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 paragraph of three sentences. It front-loads the core purpose ('Enel RJ: Download + OCR, consulta em fonte oficial') and adds only relevant caveats about payment, credentials, and LGPD. No redundant filler or jargon; each sentence earns its place.
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 has 4 parameters and no output schema, and involves external authentication and paid queries. The description covers the source, payment, and legal responsibility, but lacks details on expected output (e.g., OCR result format), failure modes, or parameter meanings. Annotations cover safety, but the operational envelope is incomplete for an agent to invoke it confidently.
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 4 parameters with zero descriptions in the schema. The description mentions 'sem credenciais da plataforma' but does not explain the individual parameters: empresa, instalacao, login_email, login_senha. It fails to clarify which are required or how they relate to the official source query, leaving the agent with little guidance.
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 states 'Download + OCR, consulta em fonte oficial' identifying the tool as a query for Enel RJ data via an official source. The name includes 'contas' (bills), which clarifies the target, but the description could more explicitly state it retrieves bill information. It distinguishes from generic sibling tools but not within a domain of similar bill-consulting 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 useful context: hosted by the platform, no platform credentials, pay per query, and queries official Brazilian sources. However, it does not explicitly state when to use this vs alternatives, nor any exclusions or prerequisites beyond payment, leaving the agent to infer the appropriate use case.
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?
Beyond the sparse annotations, the description discloses that invoke works for uninstalled MCPs as a one-off without bloating the toolkit, returns auth/checkout links requiring retry after user action, and that writes need workspace owner/admin. It also details the prompt library's shareable links that open without login, with no contradiction to 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 core purpose but is a dense, single-paragraph wall of text covering 14 actions, invoke behavior, permissions, and the prompt library. The content is valuable and each sentence adds information, but the unstructured format sacrifices conciseness and scanability.
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 breadth (14 actions, 23 optional parameters, no output schema) the description comprehensively covers high-level flows, auth, payment, one-off execution, permissions, and prompt publishing. It lacks return-value details and parameter-level specifics, but provides enough operational context for an agent to select actions and handle connect/checkout links.
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?
With 23 parameters and 0% schema description coverage, the description must compensate but only does so partially. It explains action, tool_id, arguments, and prompt-related parameters like prompt_vars and prompt_slug, but leaves many meaningful parameters (limit, immediate, tier_slug, conversation, prompt_targets, cancellation fields, report_context) entirely implicit or unexplained.
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 'The official mcp.ai marketplace — the in-platform catalog of every MCP/tool, AND the way to run them,' which clearly states the tool's resource and function. It enumerates the core search→describe→invoke flow and distinguishes sub-actions like report_bug, request_mcp, and search_prompts, separating it from sibling 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 explicit decision guidance: 'Use install only to make an MCP PERMANENT... prefer invoke for a single/occasional use' and explains when invoke returns a connect link or checkout link. It also specifies when to use list_tools, subscribe/cancel, report_bug, request_mcp, and the prompt library actions, plus ownership requirements for writes.
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 provide idempotent and non-destructive hints, so the bar is lower. The description adds context about including conversation for reproduction, but doesn't disclose any additional side effects or requirements (e.g., authentication). No contradiction found.
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 concise sentences, front-loaded with purpose. Every word serves a clear function, no 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?
Given the lack of schema descriptions and no output schema, the tool description is insufficient. It doesn't cover all parameters, nor does it explain the expected result or behavior after reporting. Leaves significant gaps for an agent relying solely on the description.
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 description must compensate. It mentions 'conversation array' but only vaguely; does not explain 'message' or 'context' parameters. Fails to adequately document all three parameters.
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?
Clearly states the action (report) and resources (bug, missing feature, feedback). Distinguishes from sibling tools like authenticate or marketplace, which serve different purposes.
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 clear context for when to use (reporting bugs/feedback) but lacks explicit exclusions or alternative tool guidance. The instruction to include the conversation array gives usage direction, though not comprehensive.
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 already declare readOnlyHint=true, idempotentHint=true, and destructiveHint=false, covering the safety profile. The description adds modest context by specifying what information is returned (platform and adapter versions), but does not elaborate on response format or other behavior. This meets baseline given annotation coverage.
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, clear, and front-loaded sentence with no wasted words. It perfectly captures the tool's purpose without 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?
For a simple, zero-parameter, read-only tool with strong annotations, this description is fully adequate. It tells the user exactly what the tool reports, and no output schema is needed. There are no gaps in context.
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 schema fully documents everything. The description correctly mentions nothing about parameters. Baseline of 4 is appropriate; no additional parameter context is needed.
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 ('Show') and resource ('current MCP platform and adapter versions'), making the tool's purpose unmistakable. It clearly distinguishes itself from sibling tools which handle authentication, connectivity, marketplace, and bug reporting.
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 clearly implies when to use the tool (whenever you need version information), which is sufficient for this trivial no-argument tool. It does not explicitly state alternatives or exclusions, but the simplicity of the tool makes this acceptable.
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 this as read-only, idempotent, and non-destructive. The description adds the specific return contents but does not disclose additional behavioral traits such as whether the status is real-time, cached, or requires network calls. This is acceptable given the annotations, but not exceptional.
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, well-structured sentence that front-loads the action and then lists the return categories. Every part is relevant and contributes to the tool's 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 a zero-parameter, read-only informational tool, the description fully covers the purpose, the return content, and the scope. There is no output schema, but the description enumerates the output fields clearly.
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 takes zero parameters, so the description does not need to explain any. According to the baseline for 0-parameter tools, this is a 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 clearly states the action ('Returns the current toolkit state') and specifies the scope ('installed MCPs, their connection status, the accounts connected to each one, and how many catalog tools each exposes'). This distinguishes it from siblings like show_version, which likely only returns version info.
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 context for when to use the tool: whenever you need an overview of the toolkit's installed MCPs, connections, and catalog tool counts. It does not explicitly mention alternatives or exclusions, but the context is unambiguous.
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
No comments yet. Be the first to start the discussion!
Related MCP Connectors
Enel RJ: Download, official-source lookup. Platform-hosted, pay per query with prepaid credit.
Enel SP: Download + OCR, official-source lookup. Platform-hosted, pay per query with prepaid credit.
Enel SP: Download, official-source lookup. Platform-hosted, pay per query with prepaid credit.
Neoenergia (Elektro): Download, official-source lookup. Platform-hosted, pay per query with prepaid
Related MCP Servers
- AlicenseNot gradedqualityCmaintenanceMCP server for downloading and extracting text via OCR from Enel São Paulo electricity bills. Read-only with magic-link authentication and pre-paid credit usage.MIT
- AlicenseNot gradedqualityCmaintenanceMCP server for consulting and downloading electricity account data from Enel São Paulo from official sources. Read-only, supports any MCP client over HTTP, uses magic-link login and pay-per-use credits.MIT
- AlicenseNot gradedqualityCmaintenanceProvides read-only consultation of Rio de Janeiro's electronic service invoices (NFS-e) from official sources, with pay-per-use credits.MIT
- AlicenseNot gradedqualityCmaintenanceRead-only MCP server for consulting CPFL utility data via official source using link and OCR, with prepaid per-query pricing.MIT
Glama MCP Gateway
Add one secure layer between your agents and this server.
TDQS
The tools are mostly distinct, but 'authenticate' and 'connect' overlap in handling authentication/connection status, and 'contas_enel_rj_download_ocr_consultar' is a specific domain tool while 'marketplace' is a meta-tool for discovering/running others, which could confuse an agent about which to use for a given task.
Naming is inconsistent: some tools use lowercase verbs (authenticate, connect, report_bug, show_version), one uses a long descriptive snake_case (contas_enel_rj_download_ocr_consultar), and 'marketplace' and 'toolkit_info' are nouns. There is no clear verb_noun pattern across the set.
Seven tools is a reasonable count for a server that combines a specific OCR/consultation service with platform management utilities. The count is not excessive, though the inclusion of meta-tools (marketplace, toolkit_info) alongside the domain tool makes the scope slightly broad.
The domain tool (contas_enel_rj_download_ocr_consultar) appears to be a single-purpose consultation, with no obvious update/delete lifecycle. The platform utilities cover auth, status, marketplace, and bug reporting, but there is no dedicated tool for managing credits or viewing history, which could be a gap for the paid-per-query model.