What can you do with this server?

Tickerr is an AI tool monitoring server providing live status, pricing, performance, and incident data for 90+ AI tools and 300+ models — no authentication required. * list_tools: List all 42+ monitored AI tools (e.g., ChatGPT, Claude, Gemini, Cursor, GitHub Copilot, DeepSeek, Groq). * get_tool_status: Get real-time operational status (OPERATIONAL / DEGRADED / DOWN), uptime %, and response time for any AI tool, checked every 5 minutes. * get_incidents: Retrieve historical outages and degradations for any AI tool over the last 90 days, sourced from 26 official provider status pages. * get_api_pricing: Fetch current input/output token costs per 1M tokens for 300+ models, filterable by model or provider, updated twice daily. * compare_pricing: Rank models by total cost for a given token workload (input + output tokens) to find the cheapest option for your use case. * get_rate_limits: Look up RPM, tokens-per-day, context window sizes, and plan tier details for any AI tool. * get_free_tier: Discover the best free plans across AI tools, filterable by category (LLM APIs, coding assistants, image generation, etc.). * get_model_performance: Get TTFT, p50/p95 latency, and tokens/sec for models. * report_incident: Anonymously report an LLM API failure and receive a fallback recommendation based on multi-agent signals.

Which integrations are available for this server?

Provides live status monitoring, incident history tracking, and service availability information for GitHub Copilot. Provides real-time status monitoring, API pricing tracking, rate limit information, and incident history for OpenAI services including ChatGPT and GPT models. Provides real-time status monitoring, uptime tracking, and service availability information for Perplexity AI.

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Tickerr - Live AI Tool Status & API Pricing

by imviky-ctrl

Overview Schema Related Servers Score Discussions

JavaScript

Remote

Tickerr MCP

Outage radar for AI agents. LLM pricing, status, inference performance, and real-time agent-reported failure signals. 9 tools. No auth required.

Install

Claude Code

claude mcp add tickerr --transport http https://tickerr.ai/mcp

Cursor / Windsurf

{
  "mcpServers": {
    "tickerr": {
      "url": "https://tickerr.ai/mcp"
    }
  }
}

Claude Desktop (stdio wrapper)

npx -y tickerr-mcp

Related MCP server: StatusCraft

Tools (9)

Tool	What it does
get_api_pricing	Current input/output/cached token costs for 300+ models. Updated twice daily.
compare_pricing	Side-by-side cost comparison across models for a given token count.
get_tool_status	Three-state verdict (OPERATIONAL / DEGRADED / DOWN). Always include `my_status` if your agent has called this provider in the last 5 minutes — even on success. Unlocks p50/p95 TTFT per model and 90-day uptime. Basic status only without it.
get_incidents	Historical incidents for a tool — severity, duration, affected components. Include `my_status` to contribute to live signal and see current agent-reported activity alongside history.
get_rate_limits	Per-plan rate limits and quotas for any AI tool.
get_free_tier	Free tier details — what's included, what's limited.
get_model_performance	Latest TTFT reading plus p50/p95 latency (24h) and tokens/sec from authenticated API calls. Use for real-time routing decisions.
list_tools	Lists all 90+ monitored tools. Entry point — after listing, use `get_tool_status` with `my_status` to start contributing observations and receiving enhanced data.
report_incident	Report an LLM API failure. Returns live signal from other agents and a routing recommendation. Anonymous.

Give and Take

Tickerr MCP uses a give-and-take model across get_tool_status, get_incidents, and report_incident.

get_tool_status — Unlock per-model latency

Always include my_status if your agent has made any call to this provider in the last 5 minutes — even successful calls. Pass is_up: true, your response_ms, and the model name.

Basic response (no my_status):

Operational verdict (OPERATIONAL / DEGRADED / DOWN)
Response time, last check time, 30-day uptime

Full response (include my_status):

Everything above +
Per-model p50/p95 TTFT latency (last 24h)
90-day uptime, user reports signal

{
  "slug": "claude",
  "my_status": {
    "is_up": true,
    "response_ms": 1400,
    "model": "claude-sonnet-4-6"
  }
}

On failure, add error_code (e.g. "529"). is_up is the only required field.

get_incidents — Contribute to live signal

Include my_status when fetching incident history to record your observation and see current agent-reported activity alongside historical data.

Without my_status: incident list + prompt to contribute.

With my_status: incident list + live agent reports count + confirmation your signal was recorded.

report_incident — Get routing intelligence

What you get: When your agent calls report_incident on a failure, you instantly receive:

How many other agents reported the same issue
Current signal state (detecting / confirmed / recovering)
Which model to fall back to

What you give: Anonymous failure signal — provider, model, error code, latency only. No request content. No personal data.

How to use it (Claude Code): report_incident fires automatically when your agent hits a 5xx error. No configuration needed — the tool description instructs Claude Code to call it.

How to use it (other agents): Add this to your system prompt:

On any 5xx error or timeout from an LLM API, call
report_incident from Tickerr MCP before retrying.
Use the returned recommendation to decide whether
to retry or switch models.

Reporting window: Active for 7 days from last Tickerr tool use. Renews automatically on any tool call.

Opt out any time: tickerr.ai/mcp/opt-out

Signal States

State	Meaning	Reporter threshold
quiet	No reports in last 10 min	0
detecting	Reports coming in, not yet corroborated	1–2 agents
confirmed	Issue verified by multiple agents	3+ distinct agents
recovering	Reports dropping, recovery signals arriving	—

Example Return Payload (report_incident)

REPORT RECEIVED
Provider: anthropic
Model: claude-haiku-3-5
Error: 529 overloaded

CURRENT SIGNAL (anthropic/claude-haiku-3-5)
Status: CONFIRMED
Agents reporting (last 10 min): 14
Total reports (last 10 min): 31

RECOMMENDATION
Action: FALLBACK
Switch to: gpt-4o-mini (openai)

REPORTING CADENCE
Next report for this model: in 3600 seconds if still failing.
Signal confirmed by multiple agents — reduce reporting frequency.

Data Coverage

Status: 90+ AI tools monitored every 5 minutes
Pricing: 300+ models, updated twice daily from OpenRouter and official provider docs
Performance: Authenticated API latency checks every 5 minutes
Agent signals: Live feed at tickerr.ai/agent-reports

Available Tools

7 tools

compare_pricingAInspect

Rank AI models by total cost for a given token workload. Useful for finding the cheapest model for your use case.

ParametersJSON Schema

Name	Required	Description
`input_tokens`	Yes	Number of input tokens per request
`output_tokens`	No	Number of output tokens per request
`filter`	No	Narrow to a provider or model family — e.g. "claude", "gpt", "gemini"
`top`	No	Show only the N cheapest models (default 10)

TDQS

A3.7/5.0

Behavior2/5

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

No annotations are present, so the description carries full burden. It only states it ranks models by cost but does not disclose how the ranking is computed, what data sources are used, or any side effects. The minimal description leaves significant behavioral gaps.

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?

Two sentences, front-loaded with the main purpose, no unnecessary words.

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 tool with 4 parameters and no output schema, the description is adequate but lacks information about what the output looks like, how models are sourced, or how the cost calculation works. It is minimally viable but not rich.

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?

Schema description coverage is 100%, so the baseline is 3. The description adds no additional meaning beyond what the schema already provides for the parameters; it merely reiterates the concept of token workload.

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?

The description clearly states the tool ranks AI models by total cost for a given token workload, which is specific and distinguishes it from sibling tools like get_api_pricing that likely just list prices.

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

Usage Guidelines4/5

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

It explicitly says 'Useful for finding the cheapest model for your use case,' providing clear context for when to use it, but does not mention when not to use it or alternatives.

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

get_api_pricingAInspect

Get current API pricing (input/output cost per 1M tokens) for AI models tracked by tickerr.ai. Filter by model or provider name.

ParametersJSON Schema

Name	Required	Description	Default
`filter`	No	Filter by model or tool name — e.g. "claude", "gpt-4o", "gemini"
`limit`	No	Max models to return (default 50)

TDQS

A3.6/5.0

Behavior2/5

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

With no annotations, the description carries the full burden. It mentions data freshness (current) and cost units, but lacks details on authentication, rate limits, or read-only nature.

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?

Two efficient sentences front-load the key purpose and filtering capability with 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 two-parameter tool with no output schema, the description adequately covers usage and filtering. Missing output format is a minor gap.

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?

Schema description coverage is 100%, so the baseline is 3. The description does not add new meaning beyond what is already in the schema for filter and limit.

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?

The description clearly states the tool retrieves current API pricing for AI models, specifying cost per 1M tokens, and allows filtering by model or provider. This distinctively separates it from siblings like compare_pricing.

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 through filter and limit parameters but does not explicitly state when to use this tool versus alternatives like compare_pricing or get_free_tier.

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

get_free_tierAInspect

Find the best free plans across AI tools, grouped by category (LLM APIs, coding assistants, image generation, etc.).

ParametersJSON Schema

Name	Required	Description	Default
`category`	No	Filter by category slug — e.g. "llm", "coding", "image", "video"

TDQS

A3.6/5.0

Behavior2/5

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

No annotations are provided, so the description must carry the full behavioral burden. It only states it 'finds' data, but does not disclose that it is read-only, requires no authentication, or any other traits. For a non-destructive query, more context is expected.

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 sentence that is front-loaded with the core purpose. No unnecessary words or redundancy.

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?

With no output schema and no annotations, the description explains the tool's purpose and parameter context well. However, it does not describe the return format or that the tool filters only free plans, which is implicit from the name.

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 single parameter 'category' is described in the schema with slug examples. The description adds value by explaining that results are grouped by category and listing example categories (LLM, coding, image, video), which goes beyond the schema's 'slug' description.

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?

The description clearly states the tool finds best free plans grouped by category, with specific examples (LLM APIs, coding assistants, etc.). This distinguishes it from sibling tools like get_api_pricing (general pricing) and list_tools (all tools).

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?

No explicit guidance on when to use this tool versus alternatives like compare_pricing or get_api_pricing. The description implies use for free plans but does not explain when not to use it or mention alternatives.

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

get_incidentsAInspect

Get historical incidents (outages, degradations) for any AI tool from the last 90 days. Sourced from 26 official provider status pages.

ParametersJSON Schema

Name	Required	Description	Default
`slug`	Yes	Tool slug — e.g. "chatgpt", "claude", "gemini"
`limit`	No	Number of incidents (default 10, max 50)

TDQS

A3.8/5.0

Behavior3/5

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

With no annotations, the description carries the burden. It discloses the read-only nature (historical, sourced from status pages) and time range, but does not mention other behavioral aspects like authentication requirements, rate limits, or potential delays in data freshness.

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?

Two sentences, each with a clear purpose: first states the core function and time range, second adds source credibility. No redundant words, front-loaded for quick understanding.

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?

The description covers the tool's purpose, scope, and data origin, which is sufficient for a simple query tool with only two parameters. However, it lacks details about the return format (e.g., fields of each incident) which would be helpful for an agent, but given no output schema, this is a minor gap.

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?

Schema description coverage is 100%, so the baseline is 3. The description adds no new meaning beyond the schema's parameter descriptions (e.g., examples for slug). It does not explain the impact of parameters on results.

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?

The description explicitly states the tool retrieves historical incidents (outages, degradations) for AI tools, with a specific time range (last 90 days) and data source (26 provider status pages). It clearly distinguishes itself from sibling tools like 'get_tool_status' (current status) and pricing 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 description implies usage for historical incident data but does not explicitly state when to use this tool versus alternatives like 'get_tool_status' for current status. No exclusions or when-not-to-use guidance is provided.

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

get_rate_limitsAInspect

Get rate limits and plan details for any AI tool — requests per minute, tokens per day, context window, and more by plan tier.

ParametersJSON Schema

Name	Required	Description	Default
`slug`	Yes	Tool slug — e.g. "cursor", "github-copilot", "chatgpt", "claude"

TDQS

A3.8/5.0

Behavior3/5

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

With no annotations, the description must disclose behavior. It mentions the return data (requests per minute, tokens per day, context window, plan tier), which is good. However, it does not state that this is a read-only operation, possible error conditions, or authentication requirements, leaving some gaps.

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, front-loaded sentence that efficiently conveys the tool's purpose and output. Every part is meaningful, with no fluff or repetition.

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?

Given no output schema and only one parameter, the description adequately explains the returned data (rate limits, tokens, context window, plan tier) and the input (slug). It is complete for a simple get operation, though more details on return structure or errors could improve it.

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 input schema already provides a description for the required 'slug' parameter. The tool description adds context by explaining that slug identifies the tool (e.g., 'cursor', 'chatgpt'), but this does not go beyond the schema's own description. With 100% schema coverage, the description adds minimal extra value.

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?

The description clearly states the tool retrieves rate limits and plan details for AI tools, using the verb 'Get' and specifying the resource. It distinguishes itself from siblings like 'get_api_pricing' and 'get_free_tier' by focusing on rate limits and plan details, not just pricing or free tiers.

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 'for any AI tool', but it does not explicitly state when to use this tool versus alternatives like 'compare_pricing' or 'get_free_tier'. It lacks explicit when-not-to-use guidance, leaving differentiation to the user.

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

get_tool_statusAInspect

Get live operational status, uptime percentage, and response time for any AI tool. Checks every 5 minutes from independent infrastructure.

ParametersJSON Schema

Name	Required	Description	Default
`slug`	Yes	Tool slug — e.g. "chatgpt", "claude", "cursor", "github-copilot", "gemini"

TDQS

A4/5.0

Behavior4/5

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

No annotations provided, so description carries full burden. It discloses update frequency (checks every 5 minutes) and data source (independent infrastructure), adding value beyond schema.

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?

Two sentences, no fluff. Front-loaded with purpose, additional info in second sentence. Every sentence earns its place.

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 tool with 1 param and no output schema or annotations, description covers purpose, update frequency, and source. Lacks return format details but sufficient given simplicity.

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?

Input schema has 100% description coverage with examples for slug. Description does not add further meaning beyond what schema already provides, baseline 3.

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?

Description clearly states the verb 'Get' and the resource: live operational status, uptime percentage, and response time for any AI tool. This distinguishes it from siblings like compare_pricing or get_incidents.

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?

Implied usage: use it to get operational status for a specific tool slug. No explicit when-to-use or alternatives mentioned, nor exclusions.

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

list_toolsAInspect

List all 42+ AI tools monitored by tickerr.ai — ChatGPT, Claude, Gemini, Cursor, GitHub Copilot, Perplexity, DeepSeek, Groq, Fireworks AI, and more.

ParametersJSON Schema

Name	Required	Description	Default
No parameters

TDQS

A3.8/5.0

Behavior2/5

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

No annotations are provided, so the description must disclose behavioral traits. It does not mention idempotency, read-only nature, rate limits, or caching. The description only states what the tool lists, not its behavior or 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.

Conciseness5/5

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

The description is a single, well-structured sentence of 25 words. It is front-loaded with the key action and provides immediate clarity without any redundant information.

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 zero-parameter, straightforward listing tool, the description is mostly complete. It tells what is listed and gives examples. However, it does not describe output format, pagination, or whether details are included, but given simplicity, this is acceptable.

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 no parameters, so schema coverage is 100%. The description adds no parameter-specific info, which is acceptable. Per rubric, baseline for 0 params is 4.

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?

The description clearly states the tool lists AI tools monitored by tickerr.ai, with specific examples like ChatGPT, Claude, Gemini, etc. The verb 'list' and resource 'AI tools' are specific, and the context distinguishes it from sibling tools that focus on pricing, status, or incidents.

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 when a user wants a list of all monitored AI tools, but does not explicitly state when to use this tool vs. alternatives like get_tool_status or compare_pricing. No exclusions or prerequisites are mentioned.

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.

7 tool updatesv1.0.1Apr 27, 2026
- First observedcompare_pricing
- First observedget_api_pricing
- First observedget_free_tier
- First observedget_incidents
- First observedget_rate_limits
- First observedget_tool_status
- First observedlist_tools

Install Server

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TDQS

A3.8/5.0

Disambiguation4/5

Tools are mostly distinct, but compare_pricing and get_api_pricing both concern pricing and could be confused. The others target separate aspects like status, incidents, rate limits, and free tiers.

Naming Consistency3/5

Most tools use 'get_' prefix, but compare_pricing uses 'compare_' and list_tools uses 'list_'. This inconsistency in naming pattern could be confusing for an agent.

Tool Count5/5

7 tools is well-scoped for monitoring AI tool status and pricing. Each tool addresses a distinct information need without being excessive or insufficient.

Completeness4/5

The set covers pricing, status, incidents, rate limits, free tiers, and a tool list. Missing perhaps a search/filter function for tools, but core needs are met.

Maintenance

ActivityInactive

ResponsivenessNo issues

Resources

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If you are the server author, to access and configure the admin panel.

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Tickerr MCP

Install

Tools (9)

Give and Take

get_tool_status — Unlock per-model latency

get_incidents — Contribute to live signal

report_incident — Get routing intelligence

report_incident — Get routing intelligence

Signal States

Example Return Payload (report_incident)

Data Coverage

Links

Available Tools

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