Parallel Search MCP
Server Details
The best web search for your AI Agent
- Status
- Healthy
- Last Tested
- Transport
- Streamable HTTP
- URL
- Repository
- parallel-web/search-mcp
- GitHub Stars
- 16
- Server Listing
- Parallel Search MCP
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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.
Tool Definition Quality
Average 3.7/5 across 2 of 2 tools scored.
web_fetch and web_search_preview serve clearly distinct purposes: one fetches content from specific URLs, the other performs web searches. No overlap or ambiguity.
Both tools follow a consistent 'web_' prefix and verb_noun pattern (fetch, search_preview). No mixing of conventions.
Two tools is the minimum viable set for search/fetch functionality, which feels slightly thin but well-scoped for the stated purpose of parallel search and content extraction.
The set covers the core workflow of searching and fetching content. Minor gaps exist (e.g., no URL listing or multi-source aggregation), but the essentials are present.
Available Tools
2 toolsweb_fetchARead-onlyInspect
Purpose: Fetch and extract relevant content from specific web URLs.
Ideal Use Cases:
Extracting content from specific URLs you've already identified
Exploring URLs returned by a web search in greater depth
| Name | Required | Description | Default |
|---|---|---|---|
| urls | Yes | List of URLs to extract content from. Must be valid HTTP/HTTPS URLs. Maximum 10 URLs per request. | |
| objective | No | Natural-language description of what information you're looking for from the URLs. Limit to 200 characters. |
Output Schema
| Name | Required | Description |
|---|---|---|
| usage | No | Usage metrics for the extract request. |
| errors | Yes | Extract errors: requested URLs not in the results. |
| results | Yes | Successful extract results. |
| warnings | No | Warnings for the extract request, if any. |
| extract_id | Yes | Extract request ID, e.g. `extract_cad0a6d2dec046bd95ae900527d880e7` |
Tool Definition Quality
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint and openWorldHint, so description need not re-state. Description adds context that it extracts content, which is consistent. No destructive behavior implied.
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?
Highly concise: two labeled sections, no wasted words. Front-loaded with purpose.
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?
Output schema exists, so return values need not be described. Description covers when to use, and tool has low complexity (2 params). Adequate.
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 3. Description adds no additional parameter details beyond the schema, but purpose is clear.
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?
Clear verb+resource: 'Fetch and extract relevant content from specific web URLs.' Distinct from sibling web_search_preview by focusing on specific URLs already identified.
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?
Explicitly lists ideal use cases: extracting from known URLs and exploring search results. Implies when not to use (for searching) but lacks explicit when-not statement.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
web_search_previewBRead-onlyInspect
Purpose: Perform web searches and return results in an LLM-friendly format and with parameters tuned for LLMs.
| Name | Required | Description | Default |
|---|---|---|---|
| objective | Yes | Natural-language description of what the web search is trying to find. Try to make the search objective atomic, looking for a specific piece of information. May include guidance about preferred sources or freshness. | |
| search_queries | Yes | List of keyword search queries of 3-6 words, which may include search operators. The search queries should be related to the objective. Limited to 3 entries of 100 characters each. |
Output Schema
| Name | Required | Description |
|---|---|---|
| usage | No | Usage metrics for the search request. |
| results | Yes | A list of WebSearchResult objects, ordered by decreasing relevance. |
| warnings | No | Warnings for the search request, if any. |
| search_id | Yes | Search ID. Example: `search_cad0a6d2dec046bd95ae900527d880e7` |
Tool Definition Quality
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint and openWorldHint. The description adds that results are 'LLM-friendly' and parameters are 'tuned for LLMs', providing some behavioral context but not going beyond what the annotations suggest.
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 one sentence, concise and front-loaded. The 'Purpose:' prefix is slightly redundant but does not detract significantly from clarity.
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 existence of an output schema and annotations, the description provides minimal but adequate context for a web search tool. It could include more details about result limits or behavior.
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 both parameters are well-documented in the schema. The tool description adds no additional parameter details beyond a generic statement about tuning, resulting in a baseline score.
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 tool performs web searches and returns results in an LLM-friendly format. However, it does not explicitly differentiate from its sibling web_fetch, which is implied by the names.
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?
No guidance on when to use this tool instead of web_fetch or any other alternative. The description lacks context for appropriate usage scenarios.
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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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
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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
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