LinkPulse
Server Details
URL reality check for agents: status, content hash, classification, wayback fallback.
- Status
- Healthy
- Last Tested
- Transport
- Streamable HTTP
- URL
- Server Listing
- LinkPulse
Available Tools
4 toolslinkpulse_checkAInspect
Fetch a URL and return its current state: HTTP status, final URL after redirects, SHA-256 content hash, classification, readability score, title/meta-description, and wayback archive fallback if dead. Cached 10 minutes.
| Name | Required | Description | Default |
|---|---|---|---|
| url | Yes | ||
| max_bytes | No | ||
| timeout_ms | No | ||
| force_fresh | No | ||
| include_body_sample | No | If true, return first 1KB of body text. |
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 adds valuable details: caching for 10 minutes, wayback fallback for dead pages, and the inclusion of a content hash and readability score. It does not mention error behaviors, rate limits, or authentication, but the disclosures given are substantive and useful.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single sentence that front-loads the core purpose and packs a detailed list of return components plus caching behavior without redundancy. Every element earns its place, making it highly efficient and easy to parse.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the moderate complexity (5 parameters, no output schema), the description is surprisingly complete: it lists the output fields, notes caching, and mentions the wayback fallback. It lacks parameter guidance and edge-case details, but the provided information covers the main behaviors needed for an agent to invoke the tool reasonably. The lack of an output schema is mitigated by listing return components.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The input schema has 5 parameters with only 20% description coverage (only include_body_sample has a description). The tool description does not explain any parameters, including max_bytes, timeout_ms, or force_fresh. Since schema coverage is low, the description should compensate but does not, leaving the agent to infer meaning from parameter names alone.
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 a specific action ('Fetch a URL') and enumerates the exact outputs returned (HTTP status, final URL, SHA-256 hash, classification, readability, title/meta, wayback fallback). This distinguishes it from siblings like linkpulse_classify, linkpulse_diff, and linkpulse_resolve by describing a comprehensive 'current state' check rather than a narrower operation.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description implies usage context: use this when you need a comprehensive snapshot of a URL's current state. However, it does not explicitly state when to prefer linkpulse_check over siblings like linkpulse_classify or linkpulse_resolve, nor does it mention any exclusions or alternatives. The 'current state' wording gives some guidance but lacks direct contrast.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
linkpulse_classifyAInspect
Classify content you already have (status code + content-type + body sample) without an outbound fetch. Useful if your agent already fetched the URL via another tool.
| Name | Required | Description | Default |
|---|---|---|---|
| status | Yes | ||
| body_sample | Yes | ||
| content_type | Yes |
TDQS
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the burden of behavioral disclosure. It does disclose the key trait that the tool makes no outbound fetch, which is critical for a classification tool. However, it does not describe the output format, potential side effects, or any error behavior, leaving notable gaps.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is two sentences long and every word earns its place. The first sentence states the action and inputs; the second provides the usage context. No redundant or filler content.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
There is no output schema, so the description should explain what the tool returns, but it does not. It adequately covers the input semantics and when-to-use guidance, but lacks information about the classification result, success/failure behavior, or edge cases. For a relatively simple tool this is a moderate 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 0%, so the description must clarify the parameters. It does so by naming all three parameters as 'status code + content-type + body sample', which maps directly to the schema properties. This adds meaningful semantics beyond the bare type definitions, though it does not provide format examples or constraints.
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 the specific verb 'Classify' with a clear resource: content the agent already has. It explicitly lists the inputs (status code, content-type, body sample) and distinguishes itself from sibling tools by stating 'without an outbound fetch', making its scope unambiguous.
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 a clear when-to-use scenario: when the agent has already fetched the URL via another tool. It implies an exclusion—do not use if an outbound fetch is needed—but does not explicitly name sibling alternatives or contrast with them beyond the fetch distinction.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
linkpulse_diffAInspect
Re-check a URL and tell you whether its content hash has changed since a previous hash you supply. Returns boolean + new hash.
| Name | Required | Description | Default |
|---|---|---|---|
| url | Yes | ||
| previous_hash | Yes |
TDQS
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the full burden. It discloses the core action (re-checking the URL) and the return value, but does not mention error handling, side effects, or whether it makes a network request. It lacks details about invalid hashes or unreachable URLs.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single sentence, front-loaded with the action, and contains no unnecessary words. It perfectly balances brevity with essential information.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a simple two-parameter tool with no output schema, the description covers the core functionality and return type. However, it omits potential error cases and does not clarify the hash algorithm, leaving some context out.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 0%, so the description must compensate. It only ties 'previous_hash' to a 'content hash' without specifying format, algorithm, or how to obtain it. The url parameter is self-explanatory from the schema, but the hash parameter lacks necessary detail.
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 specifically states the tool re-checks a URL and compares its current content hash to a supplied previous hash, returning a boolean and new hash. This clearly distinguishes it from sibling tools like linkpulse_check, linkpulse_classify, and linkpulse_resolve.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description implies usage when you have a previous hash and want to detect changes, but it does not explicitly state when to use this tool over siblings or when not to use it. No alternatives or exclusions are mentioned.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
linkpulse_resolveAInspect
Resolve redirect chain for a URL without downloading the full body. Returns final URL + redirect count.
| Name | Required | Description | Default |
|---|---|---|---|
| url | Yes |
TDQS
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the full transparency burden. It discloses a key behavioral trait: it does not download the full body, and it states exactly what it returns. This is sufficient for a simple read-only tool, though it omits edge-case behaviors like handling redirect loops or invalid URLs.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, concise sentence that fully communicates the tool's function and output without redundancy. Every word adds value, and it is 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?
For a tool with one simple parameter and no output schema, the description is complete: it explains the action, the constraint (no body download), and the return value. It provides sufficient context for an agent to invoke 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 0%, so the description must compensate for explaining parameters. It only mentions 'URL' generically, adding no detail beyond the schema's 'url' property with format 'uri'. For the single parameter, the schema already defines it, but the description should clarify expectations (e.g., absolute URL, redirect behavior), which it does not.
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 verb 'resolve' and the resource 'redirect chain for a URL', and specifies the output ('final URL + redirect count'). It effectively distinguishes this from sibling tools like 'check', 'classify', and 'diff' by focusing specifically on redirect resolution.
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 follow and resolve redirects without downloading the body, which gives clear context. It does not explicitly mention when not to use it or alternatives, but the purpose is clear enough to guide appropriate use.
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.
4 tool updates
- First observed
linkpulse_check - First observed
linkpulse_classify - First observed
linkpulse_diff - First observed
linkpulse_resolve
Frequently Asked Questions
Claiming proves that you control a remote MCP connector. It does not move, proxy, or interrupt the server.
Open the connector listing, choose Claim ownership, and sign in to Glama.
Complete one verification method:
GitHub identity — fastest for official registry listings. For a namespace such as
io.github.alice/server, link the matching GitHub user, then choose Claim with GitHub. An organization namespace such asio.github.acme/serveralso needs that organization to have installed the Glama AI GitHub App and approved its permissions, because GitHub discloses organization membership only to apps it has installed. Use HTTP or DNS when it has not.HTTP challenge — works when you can deploy a public file. Generate a token, publish the exact JSON Glama shows at
/.well-known/glama.jsonon the same origin as the connector, then choose Check HTTP challenge.DNS challenge — works when you control DNS but cannot change the server. Generate a token, create the exact TXT record Glama shows, wait for it to propagate, then choose Check DNS challenge.
After verification, Glama sends a confirmation email and gives you access to listing details, thumbnails, health checks, and analytics. Keep the HTTP file or DNS record in place: Glama periodically checks it and ownership remains verified while the token is discoverable.
The HTTP ownership file has this structure:
{
"$schema": "https://glama.ai/mcp/schemas/connector.json",
"claim": "glama_claim_..."
}Claim tokens are opaque, stable, and bound to the signed-in Glama account. They contain no email address or other personal information. If Glama can no longer discover a verified HTTP or DNS token, it starts a seven-day grace period before removing claim-based access. Restore the same token during that period to keep ownership verified. Never publish an email address, Glama session token, GitHub token, or connector credential as ownership proof.
If verification fails, confirm that you copied the current token exactly. The HTTP file must be public, return valid JSON with a successful HTTP response, and stay on the connector's origin. DNS changes may need more time to propagate. A claim cannot transfer to a different origin or hostname: if the connector target changes, Glama starts the grace period and the new target must be claimed separately after the previous claim is released.
For a connector linked to the official MCP Registry, registry updates continue to replace its name, description, and URL by default. After claiming, open Manage connector and enable Use Glama listing details as the source of truth if edits made on Glama should be preserved. Categories and thumbnails are always managed on Glama; registry linkage and technical connection settings continue to sync.
Control your server's listing on Glama, including description and metadata
Access analytics and receive server usage reports
Get monitoring and health status updates for your server
Feature your server to boost visibility and reach more users
To improve your MCP server's ranking:
Claim ownership of the server listing
Complete the server profile with an accurate description and thumbnail
Provide a test profile so Glama can connect to and evaluate the server
Keep tool definitions clear and complete to earn a high Tool Definition Quality Score (TDQS)
Route real usage through the Glama Gateway; more recorded successful server uses also improve the ranking
For users:
Full audit trail – every tool call is logged with inputs and outputs for compliance and debugging
Granular tool control – enable or disable individual tools per connector to limit what your AI agents can do
Centralized credential management – store and rotate API keys and OAuth tokens in one place
Change alerts – get notified when a connector changes its schema, adds or removes tools, or updates tool definitions, so nothing breaks silently
For server owners:
Proven adoption – public usage metrics on your listing show real-world traction and build trust with prospective users
Tool-level analytics – see which tools are being used most, helping you prioritize development and documentation
Direct user feedback – users can report issues and suggest improvements through the listing, giving you a channel you would not have otherwise
The connector status is unhealthy when Glama is unable to successfully connect to the server. This can happen for several reasons:
The server is experiencing an outage
The URL of the server is wrong
Credentials required to access the server are missing or invalid
If you are the owner of this MCP connector and would like to make modifications to the listing, including providing test credentials for accessing the server, please contact support@glama.ai.
Discussions
No comments yet. Be the first to start the discussion!
Related MCP Connectors
Wayback Machine for agents: closest archived copy of a URL with its text, and full capture history
URL intelligence for AI agents and developers. 16 tools, 25 signal weights, 20 free checks.
Free public web freshness and response-metadata checks for AI agents.
Verify a public listing URL and return source-supported facts or explicit fail-closed states.
Related MCP Servers
- FlicenseNot gradedqualityBmaintenanceA shared URL-freshness oracle for AI agents. Returns REUSE, REFETCH, or UNKNOWN before an agent pays to retrieve a URL again.-
- AlicenseAqualityDmaintenanceURL intelligence for AI agents. One URL in, structured security and data quality signals out across 7 dimensions. 13 tools, risk score 0-100 with 23 configurable weights.161101MIT
- AlicenseAqualityAmaintenanceEnables AI agents to check URL safety before fetching content, using Google Web Risk, URLhaus, PhishTank, and AI analysis to return SAFE/SUSPICIOUS/DANGEROUS verdicts.11821MIT
- AlicenseNot gradedqualityAmaintenanceEnables AI agents to investigate public URLs through evidence-first intelligence, including crawling, entity resolution, social discovery, SEO, technology detection, monitoring, reports, and optional AI reasoning.7MIT
Glama MCP Gateway
Add one secure layer between your agents and this server.
TDQS
Each tool serves a distinct operation: check fetches and analyzes a URL, classify works on already-fetched content, diff compares hashes over time, and resolve traces redirects only. The input and output contexts are clearly separated in descriptions, leaving little room for confusion.
All tools follow a uniform 'linkpulse_' prefix with a simple verb suffix: check, classify, diff, resolve. This consistent verb-only pattern makes the toolset predictable and easy to memorize.
Four tools cover the core needs of a link-checking utility without unnecessary bloat. Each tool adds a distinct capability—full inspection, offline classification, change detection, and redirect resolution—making the count well-scoped for the domain.
The tool set provides complete lifecycle coverage for the implied purpose: fetching and analyzing a link, resolving redirects, classifying content, and detecting changes. The wayback fallback in check also handles dead links, leaving no obvious dead ends or missing operations.