URL to Markdown or structured JSON (schema.org/OG); renders SPA; batch reads. No signup/key.
Claiming proves that you control this connector and unlocks listing details, thumbnails, health checks, and analytics. It does not change or interrupt the running server.
Complete one method below. Compare the methods and read troubleshooting steps.
Glama MCP Gateway
Connect through Glama MCP Gateway for full control over tool access and complete visibility into every call.
Full call logging
Every tool call is logged with complete inputs and outputs, so you can debug issues and audit what your agents are doing.
Tool access control
Enable or disable individual tools per connector, so you decide what your agents can and cannot do.
Managed credentials
Glama handles OAuth flows, token storage, and automatic rotation, so credentials never expire on your clients.
Usage analytics
See which tools your agents call, how often, and when, so you can understand usage patterns and catch anomalies.
Average 4.8/5 across 1 of 1 tools scored.
With only one tool, there is no possibility of ambiguity or overlap. The tool's purpose is clearly defined and distinct.
The single tool is named 'read', a clear and simple verb. There are no other tools to create inconsistency, so the naming is inherently consistent.
A single tool feels minimal, but for a focused web-reading/parsing service it is borderline acceptable. It would be better with a couple more tools (e.g., separate extract/jobs modes), but the count is not extreme.
The tool covers a wide range of formats (HTML, text, JSON, XML, jobs) and provides clean Markdown extraction. For its stated purpose, there are no obvious missing operations.
readAInspectFetch a web page and return its main content as clean Markdown (headings, links, lists, code preserved; nav/ads/scripts stripped). Also handles plain text, JSON and XML, extracts structured data (format=data), and normalizes any ATS/job-board feed into a unified jobs[] array (format=jobs). Use this whenever you need to read the actual content of a URL. No signup, no API key.
| Name | Required | Description | Default |
|---|---|---|---|
| url | Yes | Absolute http(s) URL to read, e.g. https://example.com/post | |
| links | No | Keep hyperlinks as [text](url). Default true. Set false for plainer prose. | |
| format | No | Output format. 'markdown' (default) = clean readable content; 'text' = plain prose, no link markup; 'data' = extract the page's embedded structured data (schema.org JSON-LD, OpenGraph, Twitter card, meta) as JSON; 'jobs' = normalize any ATS/job-board feed (Greenhouse, Lever, Ashby, Workable, SmartRecruiters, Recruitee) or a careers page with JSON-LD JobPosting into a unified jobs[] array (title, url, location, department, employment_type, updated_at, remote) — ideal for job aggregation with no per-provider parsing. | |
| render | No | JS rendering for client-side/SPA pages. 'auto' (default) renders with a headless browser only when the server HTML looks empty; 'js' forces a full browser render; 'off' never renders. The response 'rendered' field says whether a browser was used. | |
| max_chars | No | Optional cap on returned characters (content is truncated if longer). |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries full burden. It discloses that nav/ads/scripts are stripped, that JS rendering can occur ('rendered' field indicates), and that content is truncated with max_chars. This gives the agent a clear picture of the tool's behavior and limitations.
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?
Although the description is long, it is front-loaded with the core function, then systematically covers formats and parameters. The detailed explanation of 'format=jobs' earns its place given the tool's complexity. No wasted words or repetition of schema info.
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 moderate complexity (5 params, no output schema), the description covers return formats, behavioral details (rendering, truncation), and special cases (ATS/job-board feeds). It explains enough for an agent to select and invoke correctly without needing additional documentation.
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 baseline is 3. The description goes well beyond by explaining the 'format' parameter in detail (e.g., 'jobs' normalizes feeds from specific platforms into a unified schema, 'data' extracts structured metadata). This provides actionable meaning beyond the enum labels.
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 ('Fetch') and resource ('a web page'), and clearly states the main output (clean Markdown). It also enumerates additional supported formats (text, data, jobs), making the tool's purpose unambiguous even with no sibling tools to differentiate from.
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 says 'Use this whenever you need to read the actual content of a URL,' giving a direct condition for use. It also notes 'No signup, no API key' making adoption easy. However, it does not mention when not to use the tool or suggest alternative approaches, such as when only metadata is needed.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
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 or an account that owns the GitHub organization, then choose Claim with GitHub.
HTTP challenge — works when you can deploy a public file. Generate a token, publish the exact JSON Glama shows at /.well-known/glama.json on 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.
The HTTP ownership file has this structure:
{
"$schema": "https://glama.ai/mcp/schemas/connector.json",
"claim": "glama_claim_..."
}Claim tokens are opaque, bound to the signed-in Glama account, and expire after seven days. They contain no email address or other personal information. 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.
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
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.
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