gapup-mcp

workflow_orchestrator

Read-only

Meta-tool that CHAINS multiple MCP tools sequentially into a named workflow — delivering a composite output in a single call. 10 predefined workflows: compliance_full_audit (6 steps: KYC+sanctions+AI_gov+privacy+ESRS+CSRD), deal_due_diligence (7 steps: deep_dive+registry+court+patents+KYC+financials+M&A), market_entry_brief (6 steps: country_study+regulations+procurement+tax+AGOA+market_brief), competitor_intelligence_pack (5 steps: deep_dive+intel+patents+earnings+pitch_deck), esg_360 (5 steps: ESG_audit+carbon+CSRD+ESRS+supplier_esg), ip_freedom_to_operate (4 steps: patent_search+async_deep+IP_audit+competitive), climate_property_assessment (3 steps: climate_risk+real_estate+geo), pharma_target_screen (4 steps: trials+adverse_events+patents+meta_analysis), sanctions_360 (5 steps: KYC+Russian_sec+registry+crypto_wallet+court_filings), talent_market_brief (4 steps: salary+trends+adjacent_roles+skills_taxonomy). Returns steps_executed, consolidated P0/P1/P2 signals, overall_status, estimated_cost_usd, and raw outputs per step. Cache: 1h LRU per (workflow, target). Budget: 60s global timeout → partial if exceeded. Use when an agent needs a composite liverable without orchestrating tools manually.

Input Schema

TableJSON Schema

Name	Required	Description
`async`	No	If true, returns a job_id immediately (<200ms) instead of waiting for the result. Poll the result with job_result(job_id). Use for slow tools to avoid client timeouts.
`params`	No	Optional overrides passed to sub-tools. Keys depend on workflow (e.g., country, sector, role, drug, technology, wallet_address, acquirer).
`target`	Yes	The entity to analyze. A company name for most workflows; location for climate_property_assessment; role+country for talent_market_brief.
`workflow`	Yes	Named workflow to execute. Each workflow chains 3-7 tools sequentially.
`skip_failed_steps`	No	Default true: continue on step failure. Set false to abort on first error.

Output Schema

TableJSON Schema

Name	Required	Description	Default
`target`	Yes
`outputs`	Yes
`summary`	Yes
`workflow`	Yes
`overall_status`	Yes
`steps_executed`	Yes
`total_duration_ms`	Yes
`estimated_cost_usd`	Yes
`consolidated_signals`	Yes

TDQS

A4.6/5.0

Behavior5/5

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

The description adds significant behavioral context beyond the annotations: details about caching (1h LRU per workflow and target), 60s global timeout with partial results on exceedance, async mode with job_id polling, and a skip_failed_steps option. No contradiction with readOnlyHint=true (the tool orchestrates reads of other tools) and openWorldHint=true.

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 detailed but front-loaded with the core purpose and workflow list. It includes inline examples and behavioral notes. Some redundancy exists (workflow names listed twice), but overall it's efficient for the complexity. Could be slightly more concise without losing clarity.

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?

Given the tool's complexity (5 parameters, 10 workflows, async option, caching, timeout), the description is comprehensive. It covers all essential behavioral aspects, workflow enumeration, and return structure (mentioned but not detailed due to output schema existing). The description leaves no critical gaps for an AI agent to invoke this tool correctly.

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

Parameters4/5

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

While the input schema covers all 5 parameters with descriptions, the tool description adds contextual meaning: it clarifies that the 'target' parameter varies by workflow (e.g., company, location), 'params' are optional overrides, and 'skip_failed_steps' defaults to true. This supplements the schema's definitions, earning above the baseline of 3.

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 defines the tool as a meta-tool that chains multiple MCP tools into named workflows for composite outputs. It lists 10 predefined workflows with step counts, distinguishing it from sibling tools that are individual analysis tools.

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 explicitly states 'Use when an agent needs a composite deliverable without orchestrating tools manually.' It provides clear context for when to use, though it does not explicitly list when not to use or alternatives. The sibling tools include many individual tools, but the meta-tool purpose is well-conveyed.

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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TDQS

C2.5/5.0

Disambiguation2/5

With 271 tools, many have overlapping purposes (e.g., multiple competitor intel tools, multiple financial modelers, multiple ESG auditors). Detailed descriptions help slightly, but the sheer volume creates confusion. Agents would struggle to select the right tool among many similar options.

Naming Consistency1/5

Tool names are wildly inconsistent: mix of English and French, snake_case and short phrases, some very generic (process, run, execute equivalents). No discernible naming convention (e.g., abm_architect vs. boundary_control vs. bp_narratif). This makes it hard to predict tool names.

Tool Count1/5

271 tools is far beyond typical well-scoped servers (3-15). This indicates an unfocused, over-bloated tool surface. Even for a general business intelligence server, this number is excessive and violates the principle of each tool earning its place.

Completeness2/5

Despite the large count, coverage feels scattered. Some domains (e.g., content, competitive intel) have many tools, while others (e.g., supply chain, HR) have gaps. The set lacks a coherent scope; it seems like a dump of many separate tool collections rather than a complete, curated surface.

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