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mcp-knowledge

by io.github.getgapup

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
NameRequiredDescriptionDefault
asyncNoIf 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.
paramsNoOptional overrides passed to sub-tools. Keys depend on workflow (e.g., country, sector, role, drug, technology, wallet_address, acquirer).
targetYesThe entity to analyze. A company name for most workflows; location for climate_property_assessment; role+country for talent_market_brief.
workflowYesNamed workflow to execute. Each workflow chains 3-7 tools sequentially.
skip_failed_stepsNoDefault true: continue on step failure. Set false to abort on first error.

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
targetYes
outputsYes
summaryYes
workflowYes
overall_statusYes
steps_executedYes
total_duration_msYes
estimated_cost_usdYes
consolidated_signalsYes

TDQS

A4.4/5.0
Behavior4/5

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

Discloses key behavioral traits beyond annotations: 1h LRU cache per (workflow, target), 60s global timeout with partial results on timeout, and return structure (steps_executed, consolidated signals, cost, raw outputs). Annotations provide readOnlyHint=true and openWorldHint=true, which the description does not contradict.

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 dense but well-organized: it starts with the core function, lists all workflows with step details, then return format, cache, budget, and usage. Some details (e.g., full step names) could be trimmed, but it remains efficient and front-loaded with purpose.

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 as a meta-orchestrator with 5 parameters, an output schema, and no return structure in the output schema, the description fully covers workflow definitions, return structure, caching, timeout behavior, and usage guidance. It is complete and actionable.

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?

Schema coverage is 100%, providing a baseline of 3. The description adds meaningful context: workflow lists the enum values and step counts, target explains per-workflow semantics, params notes optional overrides, async describes job_id behavior, skip_failed_steps states default. This goes beyond the schema's descriptions.

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 identifies it as a meta-tool that chains multiple MCP tools into named workflows. It lists 10 predefined workflows with specific steps and constituent tools, and describes the composite output. This distinguishes it from the many individual sibling 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?

Explicitly states 'Use when an agent needs a composite deliverable without orchestrating tools manually.' This provides clear guidance on when to use. It does not explicitly mention when not to use, but the context implies alternatives are manual orchestration of individual tools.

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

C2.8/5.0
Disambiguation2/5

Many tools have overlapping purposes, especially in competitive intelligence, ESG, and risk assessment. For example, there are multiple tools for competitor analysis (competitive_deep_dive, competitor_intel, competitor_moves, etc.) with unclear boundaries. Agents would struggle to select the correct tool without deep understanding of subtle differences.

Naming Consistency2/5

Tool names are a mix of English and French, and follow no consistent pattern. Some use snake_case (e.g., abm_architect, action_plan_esg), while others are verb-focused (e.g., content_catalog, fx_rate). The lack of a uniform naming convention makes it hard for agents to predict tool names.

Tool Count1/5

With 271 tools, the server is excessively large. Even for a broad knowledge domain, this number of tools makes discovery and selection inefficient. Typical coherent servers have 3-15 tools; this has an order of magnitude more, indicating poor scoping.

Completeness3/5

The tool set covers many domains (compliance, finance, marketing, HR, etc.), but the coverage is uneven due to redundancy. Key areas have multiple overlapping tools, while some sub-domains may still have gaps. Overall, the surface is broad but not well-curated.

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