fetcher sh
Server Details
Twitter/X, YouTube, Reddit, Google and more - 100+ endpoints in total. No account, no OAuth, no subscription. Pay per call in USDC, or top up once and spend one balance across all of them.
- Status
- Healthy
- Last Tested
- Transport
- Streamable HTTP
- URL
Available Tools
5 toolscheck_balanceAInspect
Free. Show the remaining credit balance for the API key sent in this connection's Authorization header. Call this before a long run of paid requests.
| Name | Required | Description | Default |
|---|---|---|---|
No parameters | |||
TDQS
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations present, the description carries the full behavioral disclosure burden. It discloses that the call is free, that it checks the balance associated with the connection's Authorization header, and implies it is a read-only informational operation. This is good coverage for such a simple tool.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is two short sentences with no redundancy. It front-loads the key value ('Free'), states the action, and then provides a practical usage hint. Every 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?
For a zero-parameter, no-output-schema utility tool, the description covers what the tool does, its cost implication, the authentication context, and when to call it. Nothing essential is missing.
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 is empty, so there are no parameters to describe. The baseline for zero parameters is 4, and the description adds relevant context by clarifying that the balance is tied to the Authorization header rather than to an explicit parameter.
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 ('Show') and resource ('remaining credit balance for the API key'), making the tool's purpose unambiguous. It also distinguishes itself from likely siblings like topup_credits by focusing on balance checking rather than purchasing credits.
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 an explicit timing recommendation: 'Call this before a long run of paid requests.' It does not state when not to use the tool or name alternatives, but the guidance is useful and clear.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
describe_endpointAInspect
Free. Show the full call signature for one endpoint: required and optional parameters, allowed values, price, and response shape.
| Name | Required | Description | Default |
|---|---|---|---|
| path | Yes | Endpoint path, e.g. /api/twitter/search or /api/twitter/handle/{handle} |
TDQS
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the full burden. 'Free' is useful behavioral context, and 'Show the full call signature' suggests a read-only introspection operation rather than an actual endpoint call. Still, it does not explicitly state that no endpoint is executed, no credits are consumed, or that this is purely a documentation/metadata lookup.
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?
One tight sentence front-loads the most important fact ('Free') and then enumerates the return contents with no wasted words. Every phrase earns its place, and the structure makes the tool's value immediately obvious.
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 single-parameter tool with no output schema, the description is largely complete: it states cost, input is implied by the path, and it names all output categories. It could be slightly stronger by explicitly connecting to search_endpoints as the way to discover paths, but that gap is minor given the tool's simplicity.
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 already documents 'path' with an example. The tool description adds context about what the returned signature contains, but it does not add any new meaning to the path parameter beyond what the schema already provides. 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 uses a specific verb ('Show') with a clear resource ('the full call signature for one endpoint') and enumerates exactly what is included: required and optional parameters, allowed values, price, and response shape. It is immediately distinguishable from siblings like search_endpoints, which searches rather than describes a single endpoint.
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 this tool is for inspecting a specific endpoint once its path is known, and 'Free' signals it is safe for discovery. However, it never explicitly says 'if you don't know the path, use search_endpoints first' or mentions when not to use it, so the routing guidance is only implied rather than stated.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
fetch_dataAInspect
Paid. Fetch live data from any fetcher.sh endpoint. Pass the endpoint path plus its parameters (path and query parameters together in params). Priced per call in USDC on Base, Monad or Solana: pay with x402, or send an Authorization: Bearer bby_live_... header to spend prepaid credits. Call describe_endpoint first if unsure about parameters. For a named tool per endpoint, use [service].fetcher.sh/mcp.
| Name | Required | Description | Default |
|---|---|---|---|
| path | Yes | Endpoint path from search_endpoints, e.g. /api/twitter/search | |
| params | No | All parameters for the endpoint, path and query alike, e.g. { query: 'x402', sort: 'Latest' } |
TDQS
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries the behavioral disclosure burden. It clearly discloses that the tool is paid, how payment works via x402 or a Bearer bby_live_... header, and that both path and query parameters go into params. However, it does not mention potential errors, response format, rate limits, or explicitly confirm read-only behavior beyond the word 'Fetch.'
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, front-loaded with the core function, and every sentence adds necessary operational or pricing context. The standalone 'Paid.' immediately alerts the agent to the cost-bearing nature, and the subsequent sentences cover mechanics, alternatives, and payment methods without 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?
For a generic endpoint-fetching tool with no output schema, the description covers the essential operational aspects: payment, authentication, parameter construction, and when to call describe_endpoint. It does not explain the response shape or error behavior, but the arbitrary endpoint nature makes a precise return contract impractical. Overall, an agent can call it correctly with the provided information.
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 both path and params thoroughly. The description adds some clarification by stating that path and query parameters are passed together in params, but this largely mirrors the schema's existing description. It is adequate but does not substantially elevate the 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 action and resource: 'Fetch live data from any fetcher.sh endpoint.' This clearly distinguishes fetch_data from sibling tools like describe_endpoint, search_endpoints, check_balance, and topup_credits. It also clarifies the generic scope versus the named per-endpoint MCP alternative.
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 practical guidance: call describe_endpoint first if unsure about parameters, use a named tool per endpoint when one exists, and expect a paid call. It does not explicitly enumerate when not to use fetch_data, but the routing to describe_endpoint and the named-tool alternative provides usable context.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
search_endpointsAInspect
Free. Search fetcher.sh's web-data endpoints (Twitter/X, YouTube, TikTok, Instagram, Reddit, Google, Google Maps, Google News, Google Play, App Store, Yelp) by keyword and/or service. Returns paths, descriptions and per-call prices. Start here.
| Name | Required | Description | Default |
|---|---|---|---|
| query | No | Keywords, e.g. 'twitter followers' or 'reviews' | |
| service | No | Restrict to one service slug, e.g. 'twitter' |
TDQS
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations supplied, the description carries the transparency burden. It discloses that the search is free, that it is a search operation (therefore read-only in nature), and exactly what it returns: paths, descriptions, and per-call prices. It does not discuss pagination or auth, but those are not critical for this kind of catalog lookup.
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 no filler. The long service list is justified because it defines the catalog scope, and the key facts ('Free', what it returns, 'Start here') are front-loaded or clearly placed.
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, optional-parameter discovery tool with no output schema, the description says enough: what it operates on, how to scope a search, what will be returned, and the cost of the search call. It could optionally mention behavior with no query or the result-set structure, but that is a minor 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 coverage is 100%, so both parameters are already documented with examples. The phrase 'by keyword and/or service' aligns with query and service but adds no new format, defaults, or validation details; the schema already carries the load.
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 action ('Search'), a concrete resource ('fetcher.sh's web-data endpoints'), and the two search dimensions (keyword and/or service). It also states what it returns (paths, descriptions, per-call prices), which distinguishes this catalog-discovery tool from sibling tools that fetch actual data.
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?
'Start here' provides an implied usage context: this is the discovery entry point for the endpoint catalog. However, it does not explicitly say when to use a service-specific sibling such as twitter_search or describe_endpoint, nor does it tell the agent what to do after finding an endpoint.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
topup_creditsAInspect
Paid (the amount you choose, minimum $1). Buy prepaid credits with x402 so later calls skip the per-call payment round trip. With an Authorization: Bearer bby_live_... header on this connection it recharges that key. Without one, the paying wallet becomes the account and a new API key is minted and returned once — store it as a secret, it cannot be recovered.
| Name | Required | Description | Default |
|---|---|---|---|
| amount | Yes | Amount to add, in USD. Minimum 1. | |
| rotate | No | Mint a replacement key for the paying wallet, invalidating the old one immediately. Only valid when no Authorization header is sent. |
TDQS
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the full burden of behavioral disclosure. It clearly explains the payment requirement, the recharge versus new-key behavior, the one-time key return, and the critical caveat that the key cannot be recovered. This is rich, honest behavioral context.
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 yet information-dense, with every sentence contributing essential details. The key caveat about key recovery is front-loaded near the end, and there is no filler or repetition.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a tool with no output schema and no annotations, the description provides enough context to invoke it correctly: payment amount, minimum, auth modes, and outcome. It does not describe error cases or the exact response format beyond key return, but the critical operational details are present.
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 baseline is 3. The description adds meaningful context beyond the schema by explaining how the amount ties to prepaid credits, how the Authorization header changes the effect of the call, and that a new key is returned only once and must be stored as a secret. This enhances the parameter understanding.
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 ('Buy prepaid credits with x402') and a specific resource, making the tool's purpose immediately identifiable. It also explains the benefit ('later calls skip the per-call payment round trip'), which helps distinguish it from the other listed 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 gives explicit instructions for two use cases: with an Authorization header it recharges the key, and without one it mints a new API key. It does not explicitly compare against sibling tools, but the usage context is clear and actionable.
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.
11 tool updates
- Removed
appstore_apps - Removed
google_maps_place_search - Removed
google_news_search - Removed
google_search - Removed
googleplay_apps - Removed
instagram_user_handle - Removed
reddit_search_post - Removed
tiktok_post_search - Removed
twitter_search - Removed
yelp_search - Removed
youtube_search_video
16 tool updates
- First observed
appstore_apps - First observed
check_balance - First observed
describe_endpoint - First observed
fetch_data - First observed
google_maps_place_search - First observed
google_news_search - First observed
google_search - First observed
googleplay_apps - First observed
instagram_user_handle - First observed
reddit_search_post - First observed
search_endpoints - First observed
tiktok_post_search - First observed
topup_credits - First observed
twitter_search - First observed
yelp_search - First observed
youtube_search_video
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 Servers
- AlicenseNot gradedqualityDmaintenanceEnables detection and analysis of pre-public product launches through web search, content extraction, AI-powered scoring, and automated alerting. Provides comprehensive tools for surfacing stealth startup signals before they trend publicly.MIT

industrylens-mcpofficial
AlicenseNot gradedqualityBmaintenanceBrowse IndustryLens's published competitive-intelligence reports and head-to-head competitor comparisons from any AI agent — real, source-backed data.MIT- AlicenseNot gradedqualityCmaintenanceEnables AI chat clients to perform market research and competitive intelligence by gathering company overviews, competitor lists, product portfolios, pricing snapshots, and recent news via live Tavily search.MIT
- AlicenseAqualityAmaintenanceDetects hiring intent signals by scanning job boards for specific companies. Returns structured role data for outbound sales targeting.11961MIT
Glama MCP Gateway
Add one secure layer between your agents and this server.
TDQS
Each tool has a clearly distinct role: discovery (search_endpoints), inspection (describe_endpoint), data retrieval (fetch_data), balance checking (check_balance), and credit top-up (topup_credits). There is no meaningful overlap or ambiguity between them.
All tool names follow a consistent verb_noun pattern: check_balance, describe_endpoint, fetch_data, search_endpoints, topup_credits. The naming conventions are uniform and predictable throughout.
Five tools is well-scoped for this server's purpose as an API gateway and credit manager. Each tool covers a necessary part of the workflow without redundancy or bloat.
The tool set covers the full user journey: discover endpoints, inspect endpoint details, fetch data, check remaining credits, and top up credits. There are no obvious dead ends or missing operations for the stated domain.