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by ai.keenable
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This connector has been deprecated

This connect is replaced by https://glama.ai/mcp/connectors/ai.keenable.api/keenable-web-search

fetch_page_content

Read-onlyIdempotent

Fetch and extract content from a web page. Returns the page content in markdown format.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
urlYesThe URL to fetch. Example: "https://example.com"
liveNoFetch live content. Defaults to false.
promptNoOptional extraction instruction. When set, an LLM reads the fetched page and the returned content is only the output for this instruction instead of the full page. Example: "List all pricing tiers with their monthly prices".
max_charsNoMaximum number of characters of content to return. Longer content is truncated. Defaults to 50000 when omitted.
session_idNoOptional id of the agent session making this call. Send the same value on every call in one session so its requests can be grouped.
output_formatNoResponse format: 'text' (default) for readable text, or 'json' for the raw response serialized as JSON.text

Schema Changelog

Changes observed during successful MCP inspections.

  1. Changed1 schema field changed
    • addedInput schema / properties / output_format
      Added value: +{
+  "default": "text",
+  "description": "Response format: 'text' (default) for readable text, or 'json' for the raw response serialized as JSON.",
+  "enum": [
+    "text",
+    "json"
+  ],
+  "type": "string"
+}
  2. Changed1 schema field changed
    • addedInput schema / properties / session_id
      Added value: +{
+  "description": "Optional id of the agent session making this call. Send the same value on every call in one session so its requests can be grouped.",
+  "maxLength": 128,
+  "type": "string"
+}
  3. Changed1 schema field changed
    • addedInput schema / properties / prompt
      Added value: +{
+  "description": "Optional extraction instruction. When set, an LLM reads the fetched page and the returned content is only the output for this instruction instead of the full page. Example: \"List all pricing tiers with their monthly prices\".",
+  "maxLength": 2000,
+  "type": "string"
+}
  4. Changed1 schema field changed
    • addedInput schema / properties / live
      Added value: +{
+  "default": false,
+  "description": "Fetch live content. Defaults to false.",
+  "type": "boolean"
+}
  5. First observed

TDQS

A3.6/5.0
Behavior3/5

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

Annotations already declare readOnlyHint, idempotentHint, openWorldHint, and destructiveHint=false, covering the safety profile. The description adds the markdown output format, which is useful, but says nothing about live-fetch caching, truncation, or the LLM-extraction path described in the schema.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness5/5

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

Two sentences, zero waste, with the core action front-loaded before the return format. Nothing extraneous.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness4/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

With rich annotations, a fully documented 6-parameter schema, and no output schema, the definition covers what an agent needs to invoke it. The one gap is not routing between this tool and its sibling search_web_pages.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters3/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 100%, so every parameter is already documented with examples, defaults, and constraints. The description adds no parameter-level information beyond the schema, so the baseline of 3 applies.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose4/5

Does the description clearly state what the tool does and how it differs from similar tools?

States a specific verb and resource: 'Fetch and extract content from a web page,' and adds the return format (markdown). It is clearly distinguishable from the sibling search_web_pages by the fetch-vs-search verb, though it never names the sibling explicitly.

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

Usage Guidelines3/5

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

Usage is implied -- fetch when you have a URL -- but the description gives no explicit when-to-use guidance, no condition that selects it over search_web_pages, and no prerequisite or caveat language.

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

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