F
licenseNot graded
qualityD
maintenanceA remote MCP server deployable on Cloudflare Workers without authentication, providing tools for website scraping and content extraction using Cloudflare's Browser Rendering and AI.
1
-
smart-data-extractor
Server Details
smart-data-extractor MCP server on Cloudflare Workers · REST + MCP JSON-RPC · free tier
If you are the author of this connector, you can claim ownership with GitHub, an HTTP challenge, or a DNS record. Claimed connector authors can inspect health checks, view analytics, and manage their listing.
- Status
- Healthy
- Last Tested
- Transport
- Streamable HTTP
- URL
- Repository
- lazymac2x/smart-data-extractor-worker
- GitHub Stars
- 0
Available Tools
4 toolsauto_schema_learnAInspect
Idempotent · 30s timeout · Automatically infer JSON Schema from sample data without extraction. Pass idempotency_key to deduplicate within 5 minutes.
ParametersJSON Schema
| Name | Required | Description | Default |
|---|---|---|---|
| sample_data | Yes | Representative sample data as JSON string (array of objects or single object). Schema is inferred from structure; use first 1-10 rows for array samples. Max 200KB. | |
| idempotency_key | No | Optional cache key (UUID/string) for 5-minute deduplication. Repeat calls with same key return cached inferred schema instantly. |
TDQS
A4.5/5.0
Behavior5/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the full burden and succeeds by disclosing key behavioral traits: idempotency, 30-second timeout, 5-minute deduplication window, and the fact that it does not extract data. This is unusually transparent for a tool without annotations.
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?
The description is concise and front-loaded with key traits (Idempotent, 30s timeout) before stating the core function. The additional sentence about idempotency_key is necessary and directly actionable. No unnecessary words.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness5/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a tool with only two parameters and no output schema or annotations, the description covers purpose, behavioral traits, and parameter guidance. It clearly implies the return value is a JSON Schema, making the tool's behavior complete enough for an agent to use correctly.
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 coverage is 100% with detailed parameter descriptions already provided in the schema. The description adds no new parameter semantics beyond what is already in the schema, such as the idempotency_key purpose, so it meets the baseline without adding extra value.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool infers JSON Schema from sample data, with the specific verb 'infer' and resource 'JSON Schema'. It distinguishes from sibling extraction tools via the phrase 'without extraction', making its unique purpose evident.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines4/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides clear context that this tool is for schema inference rather than data extraction, which implies when to use it versus the sibling tools. It also gives explicit guidance on using the idempotency_key for deduplication. However, it does not explicitly name alternatives or state when not to use it.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
batch_extractAInspect
Idempotent · 30s timeout · Extract data from multiple sources (JSON/JSONL/text) with a single consistent schema. Pass idempotency_key to deduplicate within 5 minutes.
ParametersJSON Schema
| Name | Required | Description | Default |
|---|---|---|---|
| schema | No | Optional target JSON Schema (draft-07) applied to all sources. If omitted, inferred from first source and reused across remaining sources. Enables consistent field extraction from diverse formats. | |
| sources | Yes | Array of 1-100 data sources to extract from. Each source includes type (format) and content (raw data). | |
| idempotency_key | No | Optional deduplication key (UUID/string) for 5-minute cache. Identical batch calls (same sources + schema + key) return cached results instantly. |
TDQS
A4.2/5.0
Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
It discloses idempotency, a 30-second timeout, and a 5-minute deduplication window, which are key behavioral traits not otherwise present in annotations or schema. It does not cover error handling or partial failure behavior, which leaves some gaps for an extraction tool with no annotations.
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?
The description is a single, scannable sentence with key attributes front-loaded (idempotency, timeout) followed by the core purpose. Every element is purposeful with no redundancy.
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?
Given the absence of an output schema, the description could have hinted at the return type, but it provides essential operational context (timeout, idempotency, dedup). The rich input schema compensates for details about source limits and formats.
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?
The input schema has 100% coverage with detailed parameter descriptions, so the description adds only marginal value by referencing the consistent schema and idempotency_key. The schema itself fully explains the sources, schema, and idempotency_key semantics.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool extracts data from multiple sources (JSON/JSONL/text) with a single consistent schema. It uses a specific verb 'Extract' and names the resource, distinguishing it from siblings like extract_from_api or extract_from_url.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines4/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description implies use for batch extraction from multiple sources with a unified schema, which gives clear context. However, it does not explicitly state when to prefer this tool over alternatives or list exclusions.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
extract_from_apiAInspect
Idempotent · 30s timeout · Extract structured data from API response JSON with schema adaptation. Pass idempotency_key to deduplicate within 5 minutes.
ParametersJSON Schema
| Name | Required | Description | Default |
|---|---|---|---|
| schema | No | Optional target JSON Schema (draft-07) for field extraction. If omitted, inferred from content structure. Enforces consistent field extraction across multiple API responses. | |
| content | Yes | API response body as raw JSON string (max 200KB). Can be single object, array of objects, or array of primitives. Automatically parsed and validated. | |
| idempotency_key | No | Optional deduplication key (UUID or unique string) for 5-minute cache. Identical calls return cached result instantly. |
TDQS
A4.2/5.0
Behavior4/5
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 transparently indicates idempotency, a 30s timeout, and a 5-minute deduplication window via `idempotency_key`. These are meaningful behavioral traits beyond what the schema reveals.
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?
The description is concise and front-loaded with key information ('Idempotent · 30s timeout') followed by the core purpose. Every sentence adds value, with no redundancy or filler.
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?
Given the moderate complexity (3 parameters, no output schema), the description covers the essential purpose and behavioral traits. It could mention the return format or error handling, but the schema already handles parameter details, making the description sufficiently complete for most use cases.
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 each parameter already has a detailed description. The tool description only mentions `idempotency_key` without adding new meaning beyond the schema. Thus, it meets the baseline for high coverage but does not exceed it.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description states 'Extract structured data from API response JSON with schema adaptation'—a specific verb and resource that clearly defines the tool's function. This differentiates it from siblings like extract_from_url and batch_extract, which target different input sources or processing modes.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines4/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides clear context for use: API response JSON and schema adaptation. However, it does not explicitly state when to use this tool versus alternatives or when not to use it, so it earns a 4 rather than a 5.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
extract_from_urlAInspect
Idempotent · 30s timeout · Extract structured data from URL content with auto schema learning. Pass idempotency_key to deduplicate identical calls within 5 minutes.
ParametersJSON Schema
| Name | Required | Description | Default |
|---|---|---|---|
| url | Yes | HTTP(S) URL to fetch (will auto-download and parse), or raw content string (up to 200KB). Max 200KB after fetch. | |
| schema | No | Optional pre-defined JSON Schema (draft-07). If omitted, schema is auto-inferred from content. Provide to enforce strict field extraction and type coercion. | |
| idempotency_key | No | Optional UUID or unique identifier for 5-minute deduplication cache. Same key + tool = cached result in <5ms, zero re-fetching. |
TDQS
A3.7/5.0
Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
The description discloses idempotency, a 30s timeout, and a 5-minute deduplication cache, which are behavioral traits not covered by annotations (none provided). It does not discuss error handling, output format details, or authentication requirements, but the disclosed traits are valuable.
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?
The description is two sentences, starting with key traits (idempotent, timeout) and then the purpose. Every sentence adds distinct information; no filler.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness3/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
The tool has 3 parameters, no output schema, and no annotations. The description covers the core extraction behavior and idempotency context, but does not explain the response format, how raw content strings are handled, or error conditions. This leaves the agent with some ambiguity about the tool's full behavior.
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?
All three parameters have schema descriptions with 100% coverage, so the description adds little beyond the schema. It reinforces that omitting 'schema' triggers auto-learning and that 'idempotency_key' enables deduplication, both already in schema descriptions.
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?
The description states 'Extract structured data from URL content with auto schema learning', which is a specific action on a specific resource. It distinguishes from batch_extract by focusing on a single URL, but does not explicitly name alternative tools. The raw content string option is not mentioned in the description, only in the schema.
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?
The description provides conditional use of 'idempotency_key' to deduplicate calls, but does not explicitly state when to use this tool over sibling tools like auto_schema_learn, batch_extract, or extract_from_api. The 'auto schema learning' hint implies when to omit 'schema', but no direct alternatives are given.
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
- Changed
auto_schema_learn4 fields changed- added
Input schema / additionalPropertiesAdded value: +false - changed
Input schema / properties / idempotency_key / descriptionPrevious value: -"Optional client-supplied key for dedup within 5min cache"New value: +"Optional cache key (UUID/string) for 5-minute deduplication. Repeat calls with same key return cached inferred schema instantly." - changed
Input schema / properties / sample_data / descriptionPrevious value: -"Sample JSON data as string (array or object)"New value: +"Representative sample data as JSON string (array of objects or single object). Schema is inferred from structure; use first 1-10 rows for array samples. Max 200KB." - added
Input schema / properties / sample_data / examplesAdded value: +[ + "{\"product_id\":123,\"title\":\"Laptop Pro\",\"price\":1299.99,\"in_stock\":true}", + "[{\"id\":1,\"email\":\"alice@example.com\",\"verified\":true},{\"id\":2,\"email\":\"bob@example.com\",\"verified\":false}]", + "{\"name\":\"Report Q1\",\"sections\":[{\"title\":\"Sales\",\"value\":50000}]}" +]
- Changed
batch_extract10 fields changed- added
Input schema / additionalPropertiesAdded value: +false - changed
Input schema / properties / idempotency_key / descriptionPrevious value: -"Optional client-supplied key for dedup within 5min cache"New value: +"Optional deduplication key (UUID/string) for 5-minute cache. Identical batch calls (same sources + schema + key) return cached results instantly." - changed
Input schema / properties / schema / descriptionPrevious value: -"Optional target JSON Schema. If omitted, auto-inferred from first source."New value: +"Optional target JSON Schema (draft-07) applied to all sources. If omitted, inferred from first source and reused across remaining sources. Enables consistent field extraction from diverse formats." - changed
Input schema / properties / sources / descriptionPrevious value: -"Array of data sources (objects with type + content)"New value: +"Array of 1-100 data sources to extract from. Each source includes type (format) and content (raw data)." - added
Input schema / properties / sources / items / additionalPropertiesAdded value: +false - changed
Input schema / properties / sources / items / properties / content / descriptionPrevious value: -"The raw content to extract from"New value: +"Raw source content (max 200KB per source). For jsonl: each line must be valid JSON. For text: format \"key1:value1,key2:value2\"." - changed
Input schema / properties / sources / items / properties / type / descriptionPrevious value: -"Source format: json (single object/array), jsonl (newline-delimited), text (key:value pairs)"New value: +"Source format: \"json\" (single object or array), \"jsonl\" (newline-delimited JSON objects), \"text\" (key:value pairs delimited by commas/semicolons)" - changed
Input schema / properties / sources / items / properties / type / enumPrevious value: -[ - "json", - "text", - "jsonl" -]New value: +[ + "json", + "jsonl", + "text" +] - added
Input schema / properties / sources / maxItemsAdded value: +100 - added
Input schema / properties / sources / minItemsAdded value: +1
- Changed
extract_from_api5 fields changed- added
Input schema / additionalPropertiesAdded value: +false - changed
Input schema / properties / content / descriptionPrevious value: -"API response content (JSON string)"New value: +"API response body as raw JSON string (max 200KB). Can be single object, array of objects, or array of primitives. Automatically parsed and validated." - added
Input schema / properties / content / examplesAdded value: +[ + "{\"id\":1,\"name\":\"Alice\",\"role\":\"admin\"}", + "[{\"id\":1,\"name\":\"Alice\"},{\"id\":2,\"name\":\"Bob\"}]", + "[{\"user\":{\"id\":1,\"name\":\"Alice\"},\"status\":\"active\"}]" +] - changed
Input schema / properties / idempotency_key / descriptionPrevious value: -"Optional client-supplied key for dedup within 5min cache"New value: +"Optional deduplication key (UUID or unique string) for 5-minute cache. Identical calls return cached result instantly." - changed
Input schema / properties / schema / descriptionPrevious value: -"Optional JSON Schema. If omitted, auto-inferred from content."New value: +"Optional target JSON Schema (draft-07) for field extraction. If omitted, inferred from content structure. Enforces consistent field extraction across multiple API responses."
- Changed
extract_from_url5 fields changed- added
Input schema / additionalPropertiesAdded value: +false - changed
Input schema / properties / idempotency_key / descriptionPrevious value: -"Optional client-supplied key for dedup within 5min cache"New value: +"Optional UUID or unique identifier for 5-minute deduplication cache. Same key + tool = cached result in <5ms, zero re-fetching." - changed
Input schema / properties / schema / descriptionPrevious value: -"Optional JSON Schema. If omitted, auto-inferred."New value: +"Optional pre-defined JSON Schema (draft-07). If omitted, schema is auto-inferred from content. Provide to enforce strict field extraction and type coercion." - changed
Input schema / properties / url / descriptionPrevious value: -"HTTP(S) URL to fetch, or raw content string"New value: +"HTTP(S) URL to fetch (will auto-download and parse), or raw content string (up to 200KB). Max 200KB after fetch." - added
Input schema / properties / url / examplesAdded value: +[ + "https://api.github.com/users/octocat", + "https://example.com/page.html", + "{\"name\":\"John Doe\",\"age\":30,\"email\":\"john@example.com\"}" +]
3 tool updates
- Changed
auto_schema_learn2 fields changed- added
Input schema / properties / idempotency_keyAdded value: +{ + "description": "Optional client-supplied key for dedup within 5min cache", + "type": "string" +} - changed
Input schema / properties / sample_data / descriptionPrevious value: -"Sample JSON data as string"New value: +"Sample JSON data as string (array or object)"
- Changed
batch_extract5 fields changed- added
Input schema / properties / idempotency_keyAdded value: +{ + "description": "Optional client-supplied key for dedup within 5min cache", + "type": "string" +} - changed
Input schema / properties / schema / descriptionPrevious value: -"Target schema for extraction"New value: +"Optional target JSON Schema. If omitted, auto-inferred from first source." - changed
Input schema / properties / sources / descriptionPrevious value: -"Array of data sources"New value: +"Array of data sources (objects with type + content)" - added
Input schema / properties / sources / items / properties / content / descriptionAdded value: +"The raw content to extract from" - added
Input schema / properties / sources / items / properties / type / descriptionAdded value: +"Source format: json (single object/array), jsonl (newline-delimited), text (key:value pairs)"
- Changed
extract_from_api2 fields changed- added
Input schema / properties / idempotency_keyAdded value: +{ + "description": "Optional client-supplied key for dedup within 5min cache", + "type": "string" +} - changed
Input schema / properties / schema / descriptionPrevious value: -"Optional schema for extraction. Auto-inferred if not provided"New value: +"Optional JSON Schema. If omitted, auto-inferred from content."
4 tool updates
- First observed
auto_schema_learn - First observed
batch_extract - First observed
extract_from_api - First observed
extract_from_url
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
Cloudflare Workers MCP server: ai-gateway
Cloudflare Workers MCP server: ai-cost-optimizer
Cloudflare Workers MCP server: ai-model-router
Cloudflare Workers MCP server: api-flow-analyzer
Related MCP Servers
- AlicenseAqualityBmaintenanceWeb extraction MCP server for AI agents. Extract structured data from any URL with built-in Cloudflare bypass, JavaScript rendering, and intelligent parsing. Returns clean markdown or JSON.57942MIT
- AlicenseNot gradedqualityBmaintenanceDeployable stateless remote MCP server on Cloudflare Workers without auth, with support for registering custom tools and connecting to MCP clients.12MIT
- FlicenseNot gradedqualityCmaintenanceA stateless remote MCP server deployed on Cloudflare Workers without authentication, supporting custom tools and compatible with legacy MCP clients.-
Try in Browser
Glama MCP Gateway
Add one secure layer between your agents and this server.
TDQS
A4/5.0
Disambiguation4/5
The tools are mostly distinct: auto_schema_learn is clearly separate as it only infers schemas, while extract_from_api and extract_from_url differ by source type. batch_extract covers multi-source extraction but could overlap with single-source tools in certain scenarios. No two tools are truly indistinguishable.
Naming Consistency3/5
Naming is a mix of patterns: extract_from_api and extract_from_url share a consistent 'extract_from_X' form, but batch_extract uses an adjective+verb structure, and auto_schema_learn places the verb last. All use snake_case, which is readable, but the lack of a uniform verb_noun pattern is noticeable.
Tool Count5/5
Four tools is an ideal scope for a data extraction server, covering schema inference, single-source extraction from API and URL, and multi-source batch extraction. Each tool has a clear purpose, and the count is neither too thin nor too heavy.
Completeness4/5
The tool surface covers the core domain well: schema learning, extraction from API and URL, and batch extraction from multiple formats on a consistent schema. Minor gaps include no dedicated text extraction tool (though batch handles it) and no explicit output formatting options, but common workflows are fully supported.