AIsa YouTube Search
Server Details
Your agent needs to find video — which channels own a topic, which videos rank for a phrase, what exists in a given country and language.
What you can ask for • "Which videos rank for 'rag pipeline tutorial' in the US this month?" • "Find channels publishing about MCP, sorted by relevance." • "What playlists cover this subject in Japanese?" • "Search videos uploaded this week only."
How to use it Point any MCP client at https://mcp.aisa.one/youtube-search/mcp and sign in with OAuth — there is no key to create or paste. One search tool covering videos, channels and playlists, narrowed by country, language, upload date, duration and sort order.
It is also a door to the rest The same login reaches 26 sources and 580+ operations. Find the video here, then ask the same agent what the channel's site traffic is or what the same phrase does in Google — without adding a second server.
What it costs Finding and inspecting an operation is free. Running one is billed per call at API prices, with no seat and no monthly minimum, and every call takes max_price_usd so an agent cannot overspend by accident.
Where else it reaches https://mcp.aisa.one/social/mcp for X plus Instagram, Reddit, Pinterest and YouTube; https://mcp.aisa.one/gtm/mcp for those plus Similarweb and Apollo.
- Status
- Healthy
- Uptime
- 89.7% over 21 days
- OAuth
- Works in Glama
- Last Tested
- Transport
- Streamable HTTP · MCP 2025-11-25
- URL
TDQS
Scored across 6 tools
Each tool has a distinct role: YouTube search, catalogue search, catalogue overview, contract lookup, single execution, and batch execution. The main confusions are `search` vs `get_youtube_search` and `use` vs `batch_use`, but descriptions clarify the boundaries well enough.
Names mostly follow a lowercase snake_case verb phrase pattern (`get_details`, `list_categories`, `get_youtube_search`), but `use` and `search` are bare verbs and `batch_use` breaks the prefix style. `get_youtube_search` is also an awkward inversion of the more conventional `search_youtube`.
Six tools is a reasonable number, but five are generic AIsa platform tools wrapped around a single YouTube search operation. The set feels more like an AIsa catalogue gateway than a focused, purpose-specific YouTube Search server.
For the stated purpose of YouTube search, `get_youtube_search` covers queries, filters, pagination, and returns videos, channels, and playlists. Minor gaps exist around video/channel detail retrieval, but the catalogue `search` tool lets agents discover related operations as a workaround.
Available Tools
6 toolsbatch_useRun up to 20 operationsADestructiveInspect
Execute up to 20 operations concurrently (tool-router's batch_use). Each item answers independently; one failure never cancels the others. Billed per call to your AIsa key.
| Name | Required | Description | Default |
|---|---|---|---|
| calls | Yes | Up to 20 items of {call_id, operation_id, arguments}; steps at the same execution_level of a plan go in one batch | |
| search_id | No | search_id from the search that found these operations | |
| max_price_usd | No | Per-call price cap applied to every item |
Output Schema
| Name | Required | Description |
|---|---|---|
No output parameters | ||
TDQS
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare destructiveHint=true and openWorldHint=true, so safety is covered. The description adds valuable behavior: independence of items (one failure doesn't cancel others) and per-call billing. These are not derivable from annotations and help the agent 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?
Three short sentences with zero filler. The action and limit are front-loaded. The phrase 'tool-router's batch_use' is redundant since it restates the tool name, but it's a minor flaw. Overall it is concise and well-structured.
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?
With annotations covering destructive behavior and an output schema presumably describing results, the description covers the key operational aspects: concurrency limit, independence, and billing. It doesn't mention error reporting formats, but those likely live in the output schema. It is sufficiently complete for a batch tool.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100%, so the schema fully documents each parameter. The description adds no parameter-specific details. The calls parameter's description already explains the structure and batching context, so the baseline of 3 applies; the description doesn't need to compensate.
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 a clear action (execute) and resource (operations) with a concrete limit (up to 20) and concurrency. It doesn't explicitly name the sibling 'use' for single operations, but the distinction is clear enough from the concurrency and limit. The redundancy of 'tool-router's batch_use' is minor.
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 no guidance on when to use this tool versus the sibling 'use' tool. The schema note about 'steps at the same execution_level of a plan go in one batch' is helpful, but it lives in the schema, not the description. The description only implies batching via concurrency but doesn't state when to choose it over the single-operation alternative.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
get_detailsShow operation detailsARead-onlyInspect
Full contract of one or more operations: arguments_schema, response_schema, read_only, side_effects, availability, price, suggested_max_price_usd and known_pitfalls. Free — a quote authenticates like a call but stops before any spend.
price.model distinguishes the sources: quoted is what this
account would be charged now, list is the published price,
dynamic means the price varies with the request and only a quote
states it, composed means the operation runs several upstream
calls. suggested_max_price_usd is that estimate with headroom,
in the shape use and batch_use take as max_price_usd.
| Name | Required | Description | Default |
|---|---|---|---|
| arguments | No | The arguments the operation would be called with, for a price that reflects them. Keyed by operation_id for a batch, or passed flat for a single operation_id. Routes whose required parameters are validated before pricing have no price without them. | |
| with_quote | No | Whether each operation is priced for this account before the answer. One round trip per operation; spends nothing. | |
| operation_id | No | One operation_id from search | |
| operation_ids | No | Up to 20 operation_ids, for a batch |
Output Schema
| Name | Required | Description |
|---|---|---|
No output parameters | ||
TDQS
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint=true and destructiveHint=false. The description adds meaningful context: it is free, stops before any spend, and explains how price.model varies (quoted, list, dynamic, composed). It also clarifies that suggested_max_price_usd has headroom. This goes beyond the annotation flags and gives the agent a clear model of what happens.
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 moderately long but well structured: it opens with the core purpose, then explains the price model in a dedicated paragraph. No redundancy or filler. It front-loads the most critical information (contract fields) and then gives necessary detail about price semantics. Slightly dense but not overly verbose.
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 an output schema, so return values need no description. The description covers the key behavioral aspects (no spend, pricing models, max_price headroom) and clarifies edge cases like routes without a price. For a read-only informational tool, this is complete enough for an agent to use it correctly.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100%, so parameters are already documented. The description adds some nuance, such as how arguments affect pricing and that required parameters may be needed before a price can be quoted. It also clarifies with_quote's purpose (one round trip, spends nothing). These are useful but not essential given the schema's completeness.
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 a specific purpose: returning the full contract of one or more operations, including schemas, read_only, side_effects, price, and known_pitfalls. It clearly distinguishes this from executing operations (use, batch_use) and from discovery (search, list_categories). The verb 'get' and the noun 'details' align with the title, and the first sentence is explicit.
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 assess an operation before spending (e.g., 'A quote authenticates like a call but stops before any spend'), and the schema says 'One operation_id from search', hinting at a flow. However, it never explicitly states when to choose this over siblings like use or search, nor does it give exclusions. The guidance is implied, not stated.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
get_youtube_searchYouTube SearchARead-onlyIdempotentInspect
Search YouTube and get back matching videos, channels, and playlists. Set engine=youtube and pass the query in q; both are required. Optionally narrow by country (gl) and interface language (hl), or pass a YouTube filter token in sp for pagination and advanced filters such as upload date, duration, or result type. Use this to find video content on a topic, track a channel's recent uploads, or gauge how much video coverage a subject has. Note: this is served through the AIsa mapped path /apis/v1/youtube/search; the upstream provider's canonical path is not mounted directly.
| Name | Required | Description | Default |
|---|---|---|---|
| q | Yes | Search query. Required by runtime and upstream SearchApi. | |
| gl | No | Country code (e.g. us, jp) | |
| hl | No | Interface language | |
| sp | No | YouTube filter token (pagination or advanced filters) | |
| engine | Yes | SearchApi engine identifier. Use `youtube` for this YouTube endpoint. |
Output Schema
| Name | Required | Description |
|---|---|---|
No output parameters | ||
TDQS
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already mark this as read-only, idempotent, non-destructive, and open-worldchers. The description adds value by clarifying the required `engine` and `q` parameters)Skip; wait, that's parameter info. More importantly, it discloses the mapped path and that the upstream canonical path is not mounted, which is a useful behavioral caveat. No contradiction with annotations 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 four sentences and stays on-topic. It front-loads the main purpose)Skip; then provides required parameters, optional parameters, use cases, and a path note. It is slightly redundant with the schema's required fields and `engine` enum, but overall 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?
With an output schema present, the description need not explain return values. It adequately covers input requirements, optional parameters, practical use cases, and the mapped-path caveat. It could additionally mention pagination limits or token freshness, but for a read-only YouTube search tool this is reasonably complete.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100%, so the baseline is 3. The description mostly paraphrases the schema by restating that `engine` and `q` are required and that `gl`, `hl`, and `sp` are optional. It adds a small amount of extra meaning by giving examples of advanced filters for `sp` (upload date, duration, result type), but not enough to raise the score significantly.
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 first sentence identifies a specific action and resource: 'Search YouTube and get back matching videos, channels, and playlists.' This clearly distinguishes it from sibling tools for Instagram, Reddit, Pinterest, and Twitter search. The tool name and title reinforce the same purpose without ambiguity.
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 explicitly lists three use cases: find video content on a topic, track a channel's recent uploads, and gauge video coverage. It does not name an alternative tool or explicitly say when not to use it, but the given contexts are clear enough for an agent to select it appropriately among sibling search tools.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
list_categoriesBrowse the AIsa catalogueARead-onlyInspect
The AIsa catalogue at a glance: categories, the servers in each, tool counts, and the dedicated endpoint to connect if you only need one category. Free; no key needed. (AIsa-only: tool-router has no equivalent.)
Use mcp.aisa.one/mcp?modules=<category> (or mcp.aisa.one/<category>/mcp)
to have that category's tools listed directly instead of via search.
| Name | Required | Description | Default |
|---|---|---|---|
No parameters | |||
Output Schema
| Name | Required | Description |
|---|---|---|
No output parameters | ||
TDQS
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint=true and destructiveHint=false. The description adds meaningful behavioral context beyond annotations: the tool is free, requires no key, and can direct users to a category-specific endpoint that lists tools directly rather than through search.
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 moderately detailed but every sentence adds useful information: output scope, cost/auth, sibling differentiation, and endpoint usage. It is slightly longer than strictly necessary but remains well-structured and front-loaded with the 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 a zero-parameter, read-only tool with an output schema and safety annotations, the description is complete. It covers what the tool returns, the free/no-key access model, and provides the category endpoint for specialized use, leaving no essential gap for an agent to call it correctly.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The tool has zero parameters, so the baseline is 4. The description includes a <category> placeholder only in the endpoint examples, not as a tool parameter, which is appropriate supplementary guidance rather than a parameter-semantics gap.
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: it presents the AIsa catalogue at a glance, including categories, servers, tool counts, and a dedicated category endpoint. It also distinguishes itself from search by explaining that the endpoint lists tools directly instead of via search.
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 usage context: use list_categories for a catalogue overview, and use the provided endpoint when you only need one category. It explicitly contrasts with search ('instead of via search') and notes tool-router has no equivalent, although it does not exhaustively cover all sibling alternatives.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
searchFind AIsa operationsARead-onlyInspect
Find AIsa data operations across SEO & AI visibility, finance, social, web search & research, sales and agent mail — 950+ APIs — by describing the task. Free; no key needed.
Returns tool-router's SearchResponse: retrieval_mode (plan |
endpoint | clarification), an optional plan, and candidates with
operation_id, provider, method, path, summary, required_inputs,
price, match_reasons and details_ref — plus input_schema, so a
candidate can be passed to use without calling get_details, and
modules, the entry points that pin it.
Search spans the full AIsa catalogue, not only the category pinned
on this endpoint, so an operation is discoverable here even when it
is not in the current tools/list; a candidate whose modules does
not include the current one still runs. When more than one provider
offers the same metric, the candidates make that visible.
| Name | Required | Description | Default |
|---|---|---|---|
| limit | No | Maximum candidates, 1-10 | |
| query | Yes | What you need, in plain language, e.g. 'backlinks of a domain', 'recent tweets by a user', 'insider trades for AAPL'. English works best. | |
| category | No | Restrict to one category (seo, finance, social, search, sales, mail). Omit to search everything. |
Output Schema
| Name | Required | Description |
|---|---|---|
No output parameters | ||
TDQS
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
The description discloses meaningful behavior beyond annotations: search spans the entire AIsa catalogue, candidates may belong to modules other than the current one, multiple providers for the same metric are surfaced, and no API key is required. This gives the agent a clear picture of scope and output behavior.
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 and is dense with useful information: scope, no-auth requirement, response shape, and relationship to the catalogue. Each sentence adds operational value, and the structure makes the tool's behavior predictable.
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 complex discovery tool with output schema, the description is unusually complete: it explains the response modalities, candidate fields, direct pass-through to `use`, full-catalogue search behavior, and cross-provider visibility. An agent has enough context to invoke and interpret 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?
Schema coverage is 100%, so the schema already documents all parameters. The description adds value by explaining that the category parameter is not a hard boundary—search spans the full catalogue—and that queries are plain-language task descriptions, which clarifies how to use the tool effectively.
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 finds AIsa data operations across many categories via a plain-language query. It distinguishes itself from siblings like get_details and use by emphasizing that search covers the full catalogue, not just the pinned category.
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 conveys when to use search: when you need to discover operations across the full catalogue, even those not in the current tools/list. It also implicitly contrasts with get_details by noting that returned candidates already include input_schema, so they can be passed directly to `use` without an extra call.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
useRun an AIsa operationADestructiveInspect
Execute one AIsa operation. Billed per call to your AIsa key.
Answers in tool-router's BatchCallResult shape: successful, data or error {type, status, message, retryable}. Pinned tools in tools/list can also be called directly; this is the way to call anything found through search.
| Name | Required | Description | Default |
|---|---|---|---|
| arguments | No | Arguments matching input_schema / arguments_schema | |
| search_id | No | search_id from the search that found this operation | |
| operation_id | Yes | operation_id as returned by search | |
| max_price_usd | No | Refuse the call before any spend if it would cost more than this many USD |
Output Schema
| Name | Required | Description |
|---|---|---|
No output parameters | ||
TDQS
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already provide readOnly=false, openWorldHint=true, and destructiveHint=true. The description adds valuable behavior beyond that: billing per call, the BatchCallResult response shape, and the error structure with retryable status. It does not spell out side effects, but the destructive flag is already carried by annotations, so the additional context is sufficient.
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?
Three short sentences, each earning its place: purpose, cost, response shape, and routing guidance. Key behavioral facts are front-loaded, and nothing is redundant with the schema or annotations.
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 that an output schema exists and all parameters have descriptions, the tool description is complete enough for correct invocation. It covers cost, return/error contracts, and how routing to this tool differs from calling pinned tools directly, leaving no practical gap.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100%, and each parameter is already clearly documented: operation_id as returned by search, search_id provenance, arguments matching input_schema, and max_price_usd as a spend guard. The description does not need to add parameter detail, so baseline 3 is appropriate.
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 'Execute one AIsa operation,' a specific verb+resource statement. The word 'one' distinguishes it from the sibling batch_use, and the closing note distinguishes it from calling pinned tools directly. An agent can tell what this tool is for immediately.
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?
It explicitly states when to use the tool: 'this is the way to call anything found through search.' It also gives the alternative: 'Pinned tools in tools/list can also be called directly.' This is clear when-versus-alternative guidance with no ambiguity.
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.
6 tool updates
- First observed
batch_use - First observed
get_details - First observed
get_youtube_search - First observed
list_categories - First observed
search - First observed
use
Publisher details
- Operator
- AIsa · Publisher source
- Operator website
- https://aisa.one
- Vendor relationship
- Independent
- Documentation
- https://mcp.aisa.one/servers
- Trust center
- Not available
- Restrictions
- No paid plan, admin approval, regional limit or custom OAuth app is needed to connect. Sign-in is OAuth against auth.aisa.one with dynamic client registration (RFC 7591), or an Authorization: Bearer AIsa API key. search, get_details and list_categories are free. use and batch_use are billed per call to the caller's own AIsa key, and max_price_usd refuses anything above a cap before any spend. Some operations are subscription-only on the gateway and answer 402 without the Hive GTM Growth plan.
Related MCP Connectors
Your agent needs live data — a competitor's traffic, who to contact there, what people are saying, what Google and ChatGPT answer about you, a company's filings. Normally that is six vendor accounts, six sets of keys and six SDKs. This is one URL. **What you can ask for** • "How much traffic does stripe.com get, where does it come from, and who competes for the same keywords?" • "Find 20 Series-B fintech companies in Germany and the heads of marketing there, with emails." • "Does ChatGPT mention our brand when someone asks for the best CRM — and what does it cite?" • "What is X saying about $NVDA today, and what did the stock actually do?" • "Search the web for this, then scrape the three best pages into markdown." **How to use it** Point any MCP client at https://mcp.aisa.one/mcp and sign in with OAuth — there is no key to create or paste. Then just ask: the agent calls search to find the right operation and use to run it. **Why this rather than the source** 26 sources behind one account and one bill — DataForSEO, Semrush, Ahrefs, Similarweb, Apollo, X/Twitter, Instagram, Reddit, Pinterest, YouTube, Tavily, Exa, Perplexity, Firecrawl, CoinGecko, Kalshi, Polymarket, AgentMail and more, 580+ operations. tools/list returns five tools, not 580, so the introduction does not eat your context window. **What it costs** Finding and inspecting an operation is free. Running one is billed per call at API prices, with no seat and no monthly minimum, and every call takes max_price_usd so an agent cannot overspend by accident. **Where else it reaches** One slice at a time: https://mcp.aisa.one/seo/mcp · /finance/mcp · /social/mcp · /search/mcp · /sales/mcp · /mail/mcp · /gtm/mcp, or a single provider like /twitter-api/mcp. Same account, fewer tools listed, and search still reaches everything. Full list at https://mcp.aisa.one/servers
Your agent needs the open web — searched by more than one engine, and read as clean markdown rather than raw HTML. **What you can ask for** • "Search this question with two providers and tell me where they disagree." • "Scrape these 40 URLs into markdown, in one batch." • "Crawl this documentation site and give me every page." • "Do deep research on this topic and cite the sources." • "Find the academic papers behind this claim." **How to use it** Point any MCP client at https://mcp.aisa.one/search/mcp and sign in with OAuth — there is no key to create or paste. 30 tools across several independent providers: Tavily and Exa search, answers, contents and agent runs; Firecrawl scrape, batch scrape, crawl, map and search; Perplexity Sonar, Sonar Pro, reasoning and deep research; Oxylabs AI search and LLM jobs; OpenAI and Anthropic web search; and scholarly search. **Why this rather than the source** Several independent indexes behind one account, because one engine's blind spot is not visible from inside it. **It is also a door to the rest** The same login reaches 26 sources and 580+ operations. Find the page here, then ask the same agent who links to it or how much traffic it gets — without adding a second server. **What it costs** Finding and inspecting an operation is free. Running one is billed per call at API prices, with no seat and no monthly minimum, and every call takes max_price_usd so an agent cannot overspend by accident. **Where else it reaches** https://mcp.aisa.one/seo-serp/mcp for the Google results page itself, https://mcp.aisa.one/seo-serp-other-engines/mcp for Bing, Baidu and Naver.
Google is not the whole market. Your agent needs Bing, Yahoo, Baidu, Naver, Seznam and YouTube results — the engines that decide whether you exist in China, Korea, Japan or Central Europe. **What you can ask for** • "What ranks for this term on Baidu, and how different is it from Google?" • "Check Naver results for our Korean brand name." • "Compare Bing and Yahoo results for the same query." • "What comes up on YouTube search for this phrase in Japanese?" • "Take a screenshot of the results page as a user there sees it." **How to use it** Point any MCP client at https://mcp.aisa.one/seo-serp-other-engines/mcp and sign in with OAuth — there is no key to create or paste. 34 tools: organic results from Bing, Yahoo, Baidu, Naver and Seznam in live, regular and raw-HTML forms, YouTube search, an AI summary of a result set, and a rendered screenshot. **Why this rather than the source** The engines that matter outside the US, with the same call shape as the Google ones. **It is also a door to the rest** The same login reaches 26 sources and 580+ operations. Check the local engine here, then ask the same agent what the site's traffic or backlinks look like in that market — without adding a second server. **What it costs** Finding and inspecting an operation is free. Running one is billed per call at API prices, with no seat and no monthly minimum, and every call takes max_price_usd so an agent cannot overspend by accident. **Where else it reaches** https://mcp.aisa.one/seo-serp/mcp for Google itself. https://mcp.aisa.one/seo/mcp for all of it at once — rankings, keywords, backlinks, site health and AI-answer visibility across DataForSEO, Semrush and Ahrefs.
Your agent needs X/Twitter data — who follows a competitor, what a community is posting, who quoted that tweet, what is trending in Japan. Normally that means applying for an X developer account, passing app review, and managing a quota per endpoint. **What you can ask for** • "Who follows @stripe, and which of them are verified?" • "Pull every reply and quote on this tweet and summarise what people object to." • "List this community's moderators and its posts this week." • "What is trending in Japan right now?" • "Give me the full thread context behind this link, including the long-form article." **How to use it** Point any MCP client at https://mcp.aisa.one/twitter-api/mcp and sign in with OAuth — there is no key to create or paste. 29 read tools: users (profile, about, batch lookup by id, search, followers, verified followers, followings, follow check), tweets (timeline, latest, mentions, advanced search, replies, quotes, retweeters, thread context, articles), communities, lists, Spaces and trends. **Why this rather than the source** No developer account to apply for, no app review, no per-endpoint quota to manage. **It is also a door to the rest** The same login reaches 26 sources and 580+ operations. Ask for a handle's followers here, then ask the same agent for that brand's search traffic, its backlinks, or the people to contact — without adding a second server. **What it costs** Finding and inspecting an operation is free. Running one is billed per call at API prices, with no seat and no monthly minimum, and every call takes max_price_usd so an agent cannot overspend by accident. **Where else it reaches** https://mcp.aisa.one/social/mcp for X plus Instagram, Reddit, Pinterest and YouTube; https://mcp.aisa.one/gtm/mcp for those plus Similarweb and Apollo.
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Glama MCP Gateway
Add one secure layer between your agents and this server.