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AgentTasker MCP Server

by S3bRR
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Python
Local

AgentTasker MCP

Search the web, read websites, and run MCP tools in parallel.

Run ten searches or five calls to another MCP tool in one batch. Your agent picks concurrency up to your limit (default: 10). Background batches, search caching, rate limiting, and compact results are built in.

Python 3.10+. Zero third-party runtime dependencies. Local stdio MCP transport.

Quick start

1. Install uv and Git. uv includes uvx, which installs the server on first use. No clone needed.

2. Add AgentTasker to your coding app. Pick one option below.

Claude Code

claude mcp add --scope user --transport stdio agent-tasker -- uvx --from git+https://github.com/S3bRR/agent-tasker-mcp.git agent-tasker-mcp-server

Codex CLI / IDE extension

codex mcp add agent-tasker -- uvx --from git+https://github.com/S3bRR/agent-tasker-mcp.git agent-tasker-mcp-server

Cursor / Claude Desktop / other JSON-based clients

Create your MCP config with this JSON, or merge the entry into an existing file. Cursor uses ~/.cursor/mcp.json (global) or .cursor/mcp.json (project). Claude Desktop: Settings → Developer → Edit Config.

{
  "mcpServers": {
    "agent-tasker": {
      "command": "uvx",
      "args": ["--from", "git+https://github.com/S3bRR/agent-tasker-mcp.git", "agent-tasker-mcp-server"]
    }
  }
}

For Cursor, also add "type": "stdio" inside the agent-tasker entry.

VS Code / Copilot: copy-ready config. OpenCode: copy-ready config.

3. Restart your app and approve/enable AgentTasker if prompted. Ask:

Use AgentTasker to read https://example.com and tell me its title.

This needs no API key or search setup. You should see five tools: execute, execute_batch, get_batch, cancel_batch, and list_remote_tools.

GUI app cannot find uvx? Set command to its full path (command -v uvx on macOS/Linux, where.exe uvx on Windows). First launch needs network access; increase your client's startup timeout if necessary.

Related MCP server: local-mcp

Enable search or other MCP tools

Fetch webpages: works immediately, without an API key. Try:

Use AgentTasker to fetch these five URLs in parallel and summarize them. Use a background batch if it might take a while.

Web search: append --search-provider brave to the server arguments and set BRAVE_SEARCH_API_KEY in its environment. Or use --search-provider searxng --searxng-url http://localhost:8080 with a running SearXNG instance that enables JSON. No provider file needed. Copy-ready search setup →

Difficult websites: Markdown is preferred, with local HTML extraction otherwise. Opt in to --reader-fallback jina for hosted rendering fallback on public pages; it shares URLs with Jina and is off by default. Website reading, privacy, and curl examples →

Use AgentTasker to run ten different searches about Python asyncio with concurrency 10, in the background. Poll for results and give me a concise summary.

Other MCP tools: append --mcp-config /absolute/path/to/servers.json. Start from this example, then ask:

Use AgentTasker to call the fetch server's fetch tool for five URLs with concurrency 5.

AgentTasker starts its own configured stdio connections; it cannot reuse your harness's existing MCP sessions. Remote tools are not cached or retried because they may have side effects. Provider quotas still apply; search presets default to one request/second.

Prefer a local install?

On macOS/Linux, with Python 3.10+:

git clone https://github.com/S3bRR/agent-tasker-mcp.git
cd agent-tasker-mcp
./setup.sh --client cursor

Replace cursor with claude, codex, vscode, opencode, or generic. Paste the printed config into your app's config file, then restart the app. The installer never overwrites your settings. Keep the checkout/virtual environment in place.

# Include search or other MCP servers in the generated config:
./setup.sh --client codex --search-provider brave --mcp-config /absolute/path/to/servers.json

# Regenerate config later, without reinstalling:
.venv/bin/agent-tasker-mcp-server --print-config vscode

Windows installation and full harness setup →

Reference

Only connect servers you trust. Cancelling a batch skips queued work; already-running calls may finish. Background jobs and caches are process-local and disappear when the server restarts.

Available Tools

2 tools
executeB

Run one task and return its result.

ParametersJSON Schema
NameRequiredDescriptionDefault
nameNoTask name
task_typeYesTask type
codeNoPython code
timeoutNoTimeout seconds
urlNoTarget URL
methodNoHTTP method
headersNoHeaders
bodyNoRequest body
verify_sslNoVerify SSL
max_body_bytesNoMax response bytes
retriesNoRetry count
retry_backoff_secondsNoRetry backoff seconds
queryNoSearch query
providersNoDiscovery providers
max_resultsNoMax results
fetch_top_resultsNoFetch top result pages
fetch_max_charsNoChars per fetched page
max_linksNoMax links
max_text_charsNoMax extracted chars
include_htmlNoInclude raw HTML
extract_linksNoInclude links
extract_headingsNoInclude headings
link_include_patternNoRegex for kept links
commandNoCommand
pathNoFile path
contentNoFile content
modeNow or a
output_modeNofull or compactcompact

TDQS

B3.1/5.0
Behavior2/5

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

No annotations exist, and the description adds no behavioral context beyond the basic action. It does not disclose security requirements, side effects, rate limits, or error handling, leaving a significant gap for a tool with 28 parameters.

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

Conciseness4/5

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

The description is extremely concise (6 words) and front-loaded with the core purpose. However, for a tool with 28 parameters, slightly more detail could aid understanding without losing conciseness.

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

Completeness2/5

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

Given the high parameter count and no output schema, the description is insufficiently complete. It lacks information about return values, error scenarios, and how to properly configure the many task types.

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 baseline is 3. The description does not add any meaning beyond the schema's parameter descriptions, which are already present.

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 'Run one task and return its result' clearly states the action (run) and the resource (task), and implicitly distinguishes from sibling 'execute_batch' by specifying a single task.

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

Usage Guidelines2/5

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

No guidance is provided on when to use this tool versus execute_batch, or on prerequisites, context, or exclusions. The agent must infer usage from the name alone.

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

execute_batchA

Run many tasks in parallel and return ordered results.

ParametersJSON Schema
NameRequiredDescriptionDefault
tasksYesTask definitions
output_modeNofull or compactcompact

TDQS

A3.7/5.0
Behavior3/5

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

With no annotations, the description carries full burden but only mentions parallelism and ordered results. It omits details on error handling, concurrency limits, side effects, or ordering guarantees. Some transparency exists but significant gaps remain.

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

Conciseness4/5

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

The description is a single concise sentence covering the core functionality without redundancy. It is appropriately front-loaded, though it could benefit from a brief expansion on ordering or parallelism details without sacrificing conciseness.

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

Completeness2/5

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

Given the complexity of the tool (multiple task types, no output schema, no annotations), the description is too brief. It fails to explain ordering guarantees, error behavior, return structure, or concurrency limits, leaving the agent with insufficient context for correct invocation.

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%, so the baseline is 3. The description adds no additional meaning beyond the schema for the 'tasks' and 'output_mode' parameters, which are already well-documented in the input schema.

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 'run[s] many tasks in parallel' and returns ordered results, distinguishing it from the sibling 'execute' tool which likely handles single tasks. The verb 'run' and resource 'tasks' are specific and unambiguous.

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 usage for parallel execution of multiple tasks via the phrase 'many tasks in parallel,' which contrasts with the sibling 'execute' tool for single tasks. However, it does not explicitly state when to use this tool over alternatives or provide exclusions.

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.

  1. 2 tool updatesv1.0.1
    • First observedexecute
    • First observedexecute_batch
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TDQS

B3.4/5.0

Scored across 2 tools

Disambiguation5/5

The two tools have clearly distinct purposes: one executes a single task, the other executes multiple tasks in parallel. There is no overlap or ambiguity.

Naming Consistency5/5

Both tool names follow a consistent verb_noun pattern: 'execute' and 'execute_batch'. The batch suffix clearly indicates the parallel execution variant.

Tool Count3/5

With only 2 tools, the server feels minimal. While it covers the basic task execution needs, the scope of a 'Tasker' service might warrant additional tools for management (e.g., listing, canceling). The count is borderline but acceptable for a narrow focus.

Completeness2/5

The server lacks tools for task management beyond execution, such as listing tasks, checking status, canceling, or deleting. This creates significant gaps for an agent needing full task lifecycle support.

Maintenance

ActivityMaintained
ResponsivenessNo issues

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