deal-intel-mcp

# recruit-ai-mcp

English | Korean

Recruit AI MCP is a bootstrap fork of Deal Intelligence MCP for recruiter and search-firm intelligence.

This repository is currently a staged recruiting fork:

• Public package metadata, CLI alias, config paths, env prefix, and MongoDB defaults now use recruit-ai-mcp / recruit-ai / RECRUIT_AI_*.

• Python package imports intentionally remain deal_intel.

• The MCP tool surface now includes first recruiting workflows for candidates, client companies, positions, feedback, recommendations, metrics, reports, and demo data.

• Inherited deal-intelligence tools remain available during the staged cutover.

• Default MongoDB databases are recruit_ai and recruit_ai_demo; M0 uses the existing Python cosine search path, not Atlas Vector Search.

Recruit AI MCP is a self-owned recruiting memory and recommendation layer for solo recruiters, search firms, and small hiring teams.

It stores candidate profiles, client companies, open positions, submissions, client feedback, recruiter interactions, and recommendation runs in a database you control. It then turns that structured evidence into transparent fit signals: who matches a role, which roles fit a candidate, what is missing, what risks should be reviewed, and how client feedback changes the ranking.

For mature recruiting platforms, it is not an ATS replacement. For teams whose current system is scattered resumes, spreadsheets, intake notes, email threads, and memory, it can act as a lightweight recruiting intelligence layer: your data, your MongoDB or local storage, your fit criteria, queried from Claude Desktop, Codex, or another MCP-capable host.

The inherited Deal Intelligence MCP workflows remain available as a compatibility layer during the staged cutover. Those tools still support deal records, qualification evidence, customer themes, product context, and weekly sales reports for users who need them while the recruiting surface matures.

The default operating path is MongoDB Atlas-backed full mode, including the free/M0 tier. A bundled no-MongoDB sample mode exists for AI agents, quick evaluation, and demos, but real team use should start from full.

Start here:

• Setup with an AI assistant: AI_START_HERE.md.

• Longer full-mode walkthrough: AI_FULL_INSTALL_GUIDE.md.

• Public demo prompts: docs/public-demo-script.md.

• First external tester handoff: AI_USER_TEST_GUIDE.md.

• Fork/customize the project: docs/extending.md, docs/customization-recipes.md, then docs/architecture.md.

• Korean full-mode walkthrough: AI_FULL_INSTALL_GUIDE.ko.md.

What it does

• Stores recruiting records for candidates, client companies, positions, submissions, feedback, interactions, and recommendation runs.

• Recommends candidates for positions and positions for candidates using deterministic fit scoring plus M0-safe lexical retrieval.

• Reports recruiting pipeline counts, funnel status, feedback signals, and local Markdown/CSV recruiting reports.

• Keeps inherited deal records and customer evidence available in your MongoDB Atlas database, or in local sample/personal storage for zero-config trials during the staged cutover.

• Preserves inherited deal-intelligence compatibility for structured deal fields, health signals, follow-up gaps, customer themes, and weekly review artifacts.

• Keeps inherited product/solution context available for compatibility deal workflows, such as ICP notes, positioning, pricing notes, security claims, integrations, and competitor notes.

• Lets an AI host answer normal recruiting questions such as "which candidates best match this role?", "which roles fit this candidate?", or "generate a recruiting pipeline report".

• Keeps read-only BI, review, reporting, and export paths deterministic and LLM-free, so the host app can narrate results without extra server-side model calls.

• Exposes diagnostics (config_doctor, storage checks, usage/cost summaries) instead of hiding setup and data-quality problems.

Related MCP server: CarryMem

What it is not

• It is not an autonomous recruiter. It structures evidence; you still make the shortlist, outreach, and hiring judgment.

• It is not yet a mature CRM suite with permissions, contact ownership, calendar/email sync, workflow automation, or enterprise integrations.

• It is not a hosted SaaS that owns your recruiting/team data. The normal full path uses your MongoDB Atlas project and your selected LLM provider.

• It does not claim revenue lift numbers. If the data is incomplete, it says so.

Architecture At A Glance

This is not just a prompt wrapper. It is a small recruiting intelligence backend exposed through MCP, with inherited deal-intelligence compatibility tools still present during the staged cutover:

[AI host: Claude / Codex / ChatGPT]
        |
        v
[MCP tool surface]
        |
        v
[Domain service layer]
  |-- candidates, client companies, and positions
  |-- recruiting interactions, submissions, and feedback
  |-- fit scoring, lexical retrieval, and recommendations
  |-- recruiting metrics and reports
  |-- compatibility deal intake and sales reports
  `-- export, config, and diagnostics
        |
        v
[Storage and retrieval]
  |-- MongoDB Atlas full mode
  |-- local sample/personal mode
  `-- Atlas Vector Search pro mode

A normal recruiting recommendation path looks like this:

1. Candidate, client-company, and position records enter through recruiting tools.
2. Interactions, submissions, and client feedback add evidence over time.
3. The deterministic fit builder compares candidate and position signals.
4. Client feedback rubric deltas adjust visible dimensions with an audit ledger.
5. M0-safe lexical retrieval narrows pools before final fit scoring.
6. The MCP host narrates the recommendation, risks, and next questions.

An inherited deal-intelligence write path looks like this:

1. A meeting note, email reply, interview, or call summary enters through add_interaction.
2. The server-side LLM extracts structured deal signals from customer evidence.
3. Raw evidence, source metadata, and derived qualification fields are stored.
4. Deal summaries and product-context chunks can be embedded for retrieval.
5. Read-only tools compute metrics, gaps, reports, themes, and exports without extra LLM calls.
6. The MCP host narrates the final answer to the user.

Important boundaries:

• MCP is the interface layer; Claude, Codex, or ChatGPT can be the UI.

• Customer evidence is separated from derived intelligence such as health, gaps, themes, and qualification snapshots.

• LLM-heavy paths are mostly write/enrichment paths. Read/report/export paths are deterministic where possible.

• Product context is seller-side RAG context. It helps interpretation but is not counted as customer-stated evidence.

• sample -> full -> pro is the scaling path: zero-config trial, MongoDB-backed real data, then Atlas Vector Search when paid infrastructure is intentional.

• Tool surfaces are profile-filtered so normal users, sample users, and maintainers do not need the same visible tool set.

For the deeper module map, read docs/architecture.md. For fork/customization entry points, read docs/extending.md.

Demo Paths

Start with the recruiting demo path when showing the current product shape. The inherited deal dashboard screenshots are retained as compatibility demos for users who still rely on the staged cutover surface.

1. Recruit AI in-chat recruiting workflow

Use the bundled recruiting_pipeline_demo sample to ask:

Which candidates best match this open position?
Which open positions best fit this candidate?
What client feedback changed the recommendation?
Generate a recruiting pipeline report.

The recruiting demo shows candidate-to-position and position-to-candidate recommendations, visible fit dimensions, client-feedback adjustments, risk flags, next questions, pipeline metrics, and Markdown/CSV report export.

2. Compatibility Deal Dashboard - MongoDB Atlas Charts

Active/Attention deal counts, pipeline value by stage, qualification health-band distribution, gap distribution, and open pipeline value - all on one screen. Each chart's aggregation pipeline is generated with the render-atlas-dashboard CLI and pasted into Atlas Charts (see the "Atlas Charts Dashboard" section below).

3. Compatibility Deal Dashboard - Claude / Codex

The compatibility deal surface takes raw MCP tool output and renders win rate, the stage funnel, qualification gap patterns, data-quality coverage, and attention items right inside the conversation. It starts from pasting in a single meeting note - no extra app.

Cost-aware LLM boundary

The MCP server should calculate, store, and retrieve structured deal data. The host app - Claude Desktop, Codex, or ChatGPT - should usually explain that data to the user.

Server-side LLM calls are reserved for workflows that create persistent structured intelligence, especially add_interaction, analyze_deal, and theme backfills. Read-only BI/review/reporting tools are designed to be LLM-free so the host app can do the final narration without extra API cost.

Company names and figures in these screens are all fictional demo data.

Inherited Deal Health Compatibility

The inherited deal-intelligence tools use an active qualification framework to turn messy customer evidence into comparable deal signals. MEDDPICC is the bundled default B2B framework, and QF-v2 adds guarded custom framework support so teams can copy a preset and adapt the criteria to their own sales motion. You do not need to be a sales expert to use the compatibility tools; the user-facing output is framed as health, missing information, risk signals, customer themes, and next questions.

When you add customer evidence through inherited deal tools, the server-side LLM extracts these signals and stores the structured result. In full mode, compatibility deal data persists in MongoDB Atlas and powers pattern analysis. In optional sample mode, the same read/review surfaces run against bundled fictional data so AI agents can evaluate the tool without setup.

Product / solution context

Customer evidence and product knowledge are intentionally separate.

• Customer evidence is what prospects said in meetings, emails, interviews, or calls. It can affect qualification, customer themes, and deal health.

• Product context is seller-side knowledge: your ICP notes, product facts, pricing/packaging notes, security posture, integrations, differentiators, competitive notes, or disqualifiers. It helps the extraction prompt interpret customer evidence, but it is not counted as customer-stated evidence.

There are two normal ways to add product context.

1. Put files in a folder and tell the server where that folder is:

Use update_config(product_context_source_dirs="C:\path\to\product-docs")
Then run index_product_context(dry_run=true)
If the preview looks right, run index_product_context(dry_run=false)
Finally, run get_product_context(query="security posture for healthcare")

2. Paste product/solution text into the host app and save it as a managed note:

Use add_product_context_note(title="Healthcare security positioning",
content="...", dry_run=true)
If the preview looks right, call it again with dry_run=false and
confirmed_by_user=true.
Then run index_product_context and verify with get_product_context.

The first parser set supports txt, md, json, csv, pdf, and docx. Presentation and spreadsheet files (pptx, xlsx) currently return warnings and are planned for a later parser pass.

Product context is stored and cached locally under ~/.recruit-ai/product-context by default. Tool responses return snippets and source metadata, not full raw documents. Large catalog PDFs are supported through configurable source-file and chunk limits; check index_product_context warnings for partial_indexed when a very large file was only partly indexed. Files or pasted notes with secret-shaped content are rejected or skipped.

Product profiles

One repo, one package, three operating profiles:

MongoDB Atlas URI quick path for full:

1. Create or sign in to MongoDB Atlas: https://www.mongodb.com/cloud/atlas/register

2. Create a Free/M0 cluster.

3. Create a database user with read/write access.

4. Add your current IP address under Network Access.

5. Open Connect -> Drivers and copy the connection string.

6. Replace <password> locally and provide the URI through MCPB, .env, or your shell environment. Do not paste the URI into chat or docs.

Start humans in full. Use sample only when the user explicitly wants a zero-config trial, or when an AI agent needs to confirm the basic workflow before asking for MongoDB. It begins with bundled fictional data; once you create your own local deal, the bundled fixture is archived from the working view and your local personal dataset becomes the active dataset. Some search and LLM-heavy paths remain limited in sample mode. Move to pro only when paid infrastructure is intentional.

pro defaults to openai_api with gpt-5.4-mini for lower API cost pressure. You can still override llm.openai_api_model or switch llm.provider to anthropic in user config.

MCP tools are profile-filtered by default:

• sample: zero-config/local personal tools

• standard: normal real-data tools

• developer: all registered tools, including demo seed/cleanup helpers

Use get_tool_catalog or config_doctor to inspect the current visible count for your profile. Use tools.surface: developer or RECRUIT_AI_TOOLS_SURFACE=developer only when you intentionally want the full maintainer/debug surface.

Fork And Customize

Fork this if your recruiting workflow is too specific for a generic ATS, but too important to live only in resumes, spreadsheets, intake notes, and memory.

The repo is designed as a customizable MCP recruiting intelligence engine with inherited deal-intelligence compatibility, not only a fixed demo app. Useful fork paths include:

• solo recruiters or boutique search firms that need structured candidate and client memory before adopting a heavier ATS;

• hiring teams that want transparent candidate-position fit criteria and reusable client preference memory;

• MCP workflow builders experimenting with chat-first recruiting operations;

• teams that also want to preserve the inherited deal-intelligence workflows for sales qualification, customer themes, or weekly pipeline reports.

Common extension seams:

• recruiting fit rubric dimensions and scoring criteria;

• candidate, client-company, position, and submission schemas;

• profile and tool-surface visibility;

• storage backends and MongoDB operational contracts;

• server-side LLM providers and cost tracking;

• report/export formats;

• product/solution context parsers;

• new MCP tools for team-specific workflows.

This project is MIT-licensed. You may use, copy, modify, merge, publish, distribute, sublicense, and sell modified versions, subject to the license terms. Keep the license and attribution notices when redistributing a fork.

For implementation entry points, read:

• docs/extending.md - extension seams and contracts;

• docs/customization-recipes.md - practical fork recipes;

• docs/architecture.md - module ownership, tool ownership, and change playbooks.

Install Overview

The normal product path is full: MongoDB Atlas-backed real recruiting/team data, an MCP host, and one LLM credential path for extraction.

If you are a non-developer, the easiest route is to ask your AI assistant to read AI_START_HERE.md. It will walk you through Python, Node.js, MongoDB Atlas, and Claude/Codex/ChatGPT setup.

Prerequisites

• Python 3.11+ in a conda environment

• Node.js 18+ for the no-git-clone npx bootstrapper

• One MCP chat client: Claude Desktop, or Codex/ChatGPT with MCP support

• For full: MongoDB Atlas account, Free/M0 cluster, and MONGODB_URI

• For LLM extraction/scoring: ChatGPT OAuth from a compatible subscription, ANTHROPIC_API_KEY, or OPENAI_API_KEY

If you install Miniconda/Anaconda for the first time, the first conda create or conda install may ask you to accept Anaconda Terms of Service for the default package channels, commonly https://repo.anaconda.com/pkgs/main and https://repo.anaconda.com/pkgs/r. Read the prompt and enter a to accept, or run conda tos accept from Terminal/PowerShell after reviewing the terms. See Anaconda's command-line ToS guide: https://www.anaconda.com/docs/getting-started/tos-plugin.

Short version:

Install Python and Node.js, prepare a MongoDB Atlas M0 connection string,
connect an MCP host, and choose one LLM path. Then use npx or an editable
Python install.

npx install path

After the recruit-ai-mcp@0.1.0 npm/PyPI packages are published and public fresh-smoke evidence is recorded, most users should start here:

npx recruit-ai-mcp setup --python /path/to/python
npx recruit-ai-mcp mcp-config

Use the Python path printed by mcp-config in Claude Desktop MCPB. Set MONGODB_URI through the MCPB install form, .env, or your shell environment. On Windows, use npx.cmd if PowerShell blocks npx. Until the public registry publication is complete, use the git clone/customization path below or the maintainer local-wheel smoke path in docs/bootstrapper-fresh-smoke.md.

Detailed install guides:

• AI_START_HERE.md - canonical AI-agent setup path, including npx, editable install, full/sample choice, and first checks

• AI_FULL_INSTALL_GUIDE.md - longer full-mode walkthrough for non-developer users

• mcpb/README.md - Claude Desktop MCP bundle

Git clone / customization path

Clone or download this repository when you want to inspect or modify prompts, reports, storage, qualification frameworks, or MCP tools. From the repository root:

~/miniconda3/envs/deal-intel/python.exe -m pip install -e ".[embedding]"

Replace the Python path with the interpreter where you want the package installed. Adding [embedding] installs local semantic-search dependencies.

After install, check the effective config:

recruit-ai config profiles
recruit-ai config show

Readiness check

Run these before troubleshooting deeper issues:

recruit-ai config doctor --offline
recruit-ai smoke-profile --profile full --offline

When network access to Atlas is available, run a live storage ping:

recruit-ai storage-status

Use config_doctor from the MCP host after installing the bundle. It is the first recovery tool when paths, profiles, MongoDB, or LLM readiness are unclear. When config_doctor is OK, add the first recruiting records before expecting meaningful recommendations: create a client company, create a position, create a candidate, add recruiting evidence with add_recruiting_interaction, then run recommend_candidates_for_position. Inherited deal-intelligence workflows are still available for compatibility; for those, create or select a deal, store customer evidence with add_interaction, then run get_deal_review.

Optional zero-config smoke

$env:RECRUIT_AI_STORAGE_BACKEND='local_sample'
recruit-ai smoke-profile --profile sample
recruit-ai storage-status
recruit-ai smoke-natural-questions --pack recruiting --as-of 2026-06-22
recruit-ai smoke-natural-questions --as-of 2026-06-10

Use this only for zero-config evaluation. It starts with bundled fictional data and does not require MongoDB, paid APIs, or Atlas Vector Search.

full starts with the user's own MongoDB data and does not auto-seed sample records. If you want a richer Atlas-backed demo, switch to the developer tool surface and run create_sample_data; it can write either weekly_pipeline_demo deal records or recruiting_pipeline_demo recruiting records to the configured demo database, not to the primary real-data database.

Zero-config sample mode (no MongoDB)

If you only want to test the BI and deal-review flows, you can run the bundled fictional sample dataset without MongoDB Atlas, API keys, or Atlas Vector Search.

Temporary PowerShell session:

$env:RECRUIT_AI_STORAGE_BACKEND='local_sample'
& "$HOME\miniconda3\envs\deal-intel\python.exe" -m deal_intel.cli storage-status
& "$HOME\miniconda3\envs\deal-intel\python.exe" -m deal_intel.cli smoke-natural-questions --pack recruiting --as-of 2026-06-22
& "$HOME\miniconda3\envs\deal-intel\python.exe" -m deal_intel.cli smoke-natural-questions --as-of 2026-06-10

Persistent sample profile:

recruit-ai config init --profile sample --dry-run
recruit-ai config init --profile sample

Sample mode is intentionally limited, but it is no longer purely read-only. Core dashboard, reporting, customer-theme, deal-review, create/update/stage, interaction-ingestion, and lifecycle flows can run against local personal data. add_interaction requires a ready LLM provider and works on user-created local deals; use interaction_type: meeting for meeting notes. Local sample mode skips embedding storage and does not expose raw content in list/BI/report paths. Semantic search_deals, Atlas Charts, and shared team operation still belong to MongoDB-backed full or pro mode.

Local personal data defaults to ~/.recruit-ai/local-data and can be changed with storage.local_data_dir.

Useful local-data commands:

recruit-ai local-data status
recruit-ai local-data export
recruit-ai local-data reset          # dry-run
recruit-ai local-data reset --force  # clears local deals/recruiting, preserves audit logs
recruit-ai local-data migrate-to-mongo          # dry-run
recruit-ai local-data migrate-to-mongo --apply  # writes local records to MongoDB

The bundled fictional deal fixture is immutable. After local personal deals exist, the fixture is hidden from active deal reads instead of being mixed with your data. Zero-config recruiting records are stored in local recruiting.json, so you can create a client, role, candidate, feedback, and first recommendation without MongoDB. The dry-run-first local-to-MongoDB migration command migrates user-created local personal deals and recruiting records, never bundled fixture records or local delete audit logs. If no local personal records exist yet, the dry-run returns immediately and skips MongoDB target readiness checks.

Tool guide

The detailed guide below focuses on the core user-facing workflow. For the complete current tool contract, read docs/baseline.md.

Tip: In Claude Desktop, type the example sentences below verbatim or say something similar. For recruiting workflows, start with a client company, position, and candidate before asking for recommendations.

Recruiting-first workflow

The current recruit-ai surface supports an evidence-first recruiting loop while the inherited deal-intelligence tools remain available for compatibility.

Use this sequence for a new search:

1. create_client_company - create the hiring customer.

2. create_position - create the open role or search mandate.

3. create_candidate - add candidate profiles.

4. add_recruiting_interaction - add screens, client intake notes, interviews, email threads, call summaries, or internal notes.

5. add_client_feedback - capture client response and reusable preference learning.

6. recommend_candidates_for_position or recommend_positions_for_candidate

   • rank fit in either direction.

7. get_recruiting_metrics - inspect funnel, status, feedback, and data quality.

8. export_recruiting_report - write local Markdown and CSV pipeline artifacts.

These recruiting tools are deterministic today. They do not call LLMs, embeddings, or Atlas Vector Search, and they work on Atlas M0 with the existing Python ranking path.

create_client_company

When to use: Before creating positions for a hiring customer or saving client preference memory.

Example:

Create client company Northstar Health. Industry healthcare technology,
stage growth, locations Boston and Remote US. Hiring preferences: values
healthcare data platform experience and written architecture rationale.

Useful fields include name, industry, stage, locations, hiring_preferences, and risk_notes.

create_position

When to use: To define the role/search mandate the recruiter is trying to fill.

Example:

Create a position for Northstar Health: Senior Backend Platform Engineer.
Must have Python, data platforms, and healthcare. Nice to have Kafka and HIPAA.
Target compensation 180k to 230k USD. Remote-friendly, Boston or Remote US.

The position stores role requirements, target compensation, location/remote constraints, and the default recruiting fit rubric: skill_fit, domain_fit, seniority_fit, compensation_fit, location_fit, availability_fit, client_preference_fit, and risk.

create_candidate

When to use: To add or update a candidate profile before matching.

Example:

Create candidate Avery Chen. Current title Staff Backend Engineer at Clearpath
Systems. Skills Python, FastAPI, PostgreSQL, Kafka, HIPAA. Domains healthcare
and data platforms. Seniority staff. Locations Boston and Remote US.
Availability 30 days.

Candidate records store skills, domains, seniority, locations, work authorization, availability, compensation expectation when known, preferences, risk flags, and evidence references.

add_recruiting_interaction

When to use: To attach evidence to a candidate, client company, position, or submission.

Example:

Add a candidate_screen interaction for cand_avery_chen. Summary: Avery wants
staff-level backend platform work, is strongest in healthcare data workflows,
and can start in about 30 days.

raw_content may be stored, but normal responses hide it. This keeps evidence available for future internal workflows without leaking raw notes in ordinary tool output.

add_client_feedback

When to use: After a client, hiring manager, candidate, or recruiter gives feedback on a submission, candidate, or position.

Example:

Add positive feedback for submission sub_avery_northstar_backend. Decision
signal advance. Summary: hiring manager liked the healthcare platform depth.
Rubric deltas: domain_fit +1 and client_preference_fit +1.

Feedback can include rubric_deltas so repeated client preference patterns adjust future fit scoring transparently. If feedback is attached to a submission, the service links the feedback id back to that submission when the submission exists.

Recommendations

Use recommend_candidates_for_position(position_id=...) when a client asks "who should we show for this role?" Use recommend_positions_for_candidate(candidate_id=...) when a candidate asks "which open searches fit me?"

Both tools:

• use M0-safe lexical retrieval as a prefilter;

• score final fit with the deterministic recruiting rubric;

• include reasons, low-fit rejection notes, risk flags, and next questions;

• preview by default;

• write a recommendation_runs record only when save_run=true.

Recruiting metrics and report export

Use get_recruiting_metrics for chat answers about open positions, submissions, placements, feedback rates, and data quality. Use export_recruiting_report when you need local Markdown/CSV artifacts for a pipeline review.

Example:

Show recruiting pipeline metrics.
Export the recruiting pipeline report for today.

Recruiting demo data

For an Atlas-backed fictional recruiting demo, switch to the developer surface and run:

create_sample_data(dataset="recruiting_pipeline_demo")

The first call should stay dry-run. Real writes require dry_run=false and confirmed_by_user=true, and they go to the configured demo database, not the primary real-data database.

Inherited deal workflow

The remaining guide covers the inherited deal-intelligence workflow that still ships during the staged recruiting cutover. Use it only when the user is working with sales/deal records rather than recruiting candidates and roles.

You can find a deal_id with create_deal or list_deals.

1. create_deal - create a new deal

When to use: Run this first when you start engaging a new prospect.

Example:

Create a new deal for Hyundai Precision. Manufacturing industry, deal size 200M KRW.

Parameters:

Example result:

{
  "ok": true,
  "deal_id": "a3f9...",
  "company": "Hyundai Precision",
  "industry": "Manufacturing",
  "industry_tags": ["Manufacturing"],
  "customer_segment": "enterprise",
  "deal_size_amount": 200000000,
  "deal_size_currency": "KRW",
  "deal_size_status": "rough_estimate",
  "expected_close_date": "2026-06-15",
  "expected_close_date_source": "config_default"
}

Remember this deal_id, or look it up later with list_deals.

If you omit the expected close date, a default of 7 days after creation is filled in. This is an operational default, not a confirmed schedule. A date you provide always takes precedence over config.

When you enter a deal amount, you must also set its status. If unknown, leave deal_size_status="unknown" and leave the amount blank. If only a positive amount is given, the tool asks which basis applies - sales estimate / customer budget / quote sent. If only 0 is given, it doesn't save immediately and asks whether it's a strategic free/reference deal or an undecided amount. If undecided, it's saved as unknown with the amount blanked. An intentional zero-value deal (free sample, reference win) is saved with deal_size_amount=0 and deal_size_status="strategic_zero". If you heard a customer budget or sent a quote, use customer_budget or quoted so it counts as a validated pipeline value in metrics. Mixed currencies are not silently summed; metric and report outputs expose currency fields or per-currency breakdowns.

deal_value:
  default_currency: KRW

pipeline:
  expected_close:
    default_days: 7
    days_by_segment:
      public_sector: 60
      enterprise: 28
    days_by_industry:
      Government: 60
      Manufacturing: 28

reporting:
  timezone: Asia/Seoul

Keep industry as the single primary business vertical. If an account is cross-industry, put the other verticals in industry_tags; the primary industry is always included in that tag list. Put account maturity, ownership, buying segment, or funding stage in customer_segment instead. Industry input is normalized against the built-in taxonomy when possible, so values such as 제조, 핀테크, or 보험·금융·대기업 are stored as canonical metadata such as industry=Insurance, industry_tags=["Insurance", "Finance"], and customer_segment=enterprise. Segment overrides apply first; industry overrides apply second, both on a case-insensitive exact match. Auto-dates use the business date in the reporting timezone, while stored audit timestamps stay in UTC.

For existing data, use the taxonomy cleanup CLIs:

recruit-ai audit-taxonomy
recruit-ai apply-taxonomy-cleanup
recruit-ai apply-taxonomy-cleanup --apply --confirmed-by-user
recruit-ai backfill-industry-tags
recruit-ai backfill-industry-tags --apply --confirmed-by-user

audit-taxonomy is read-only. apply-taxonomy-cleanup and backfill-industry-tags are dry-run by default. They automatically normalize recognizable mixed labels into primary industry, industry tags, and customer segment. If an industry is missing, the tool treats it as an enrichment task: it either drafts a medium-confidence industry from the company name or returns a web research query so the AI client can look it up and call update_deal. The default UX is draft-first and correction-friendly; only impossible rows stay out of writes.

2. add_interaction - add a customer interaction

When to use: Right after a customer meeting, email reply, user interview, call summary, or internal note. Paste the content as-is and the server-side LLM extracts active-framework qualification signals and customer themes with source-aware scoring. MEDDPICC is the default built-in framework.

Cost note: this is intentionally one of the few places where the MCP server uses its own LLM provider, because the extracted result is persisted as product data. For explanation-only questions, prefer read tools such as get_deal_review, get_deal_gaps, and get_metrics; let Claude/Codex explain their deterministic output.

Meeting notes are just interaction_type: meeting. Older meetings records are still read as legacy fallback, but new integrations should write canonical interactions records through this tool.

Example:

Add today's (2026-06-08) meeting note to Hyundai Precision, deal_id: a3f9...
Use interaction_type=meeting and direction=inbound.

Notes:
Met Director Kim (purchasing decision-maker). Current production-line defect rate
is 3.2%, causing ~1.5B KRW/yr loss. Our solution targets <=1.5%. Manager Park is in
favor internally. Competitor A is under review but costs 2x. Internal approval due
end of June.

Parameters:

Safety and cost guardrails:

• content is capped at 20,000 characters before any LLM call.

• Identical same-day, same-type, same-direction content is skipped before any LLM call unless allow_duplicate=true.

• Interaction content and seller-side product context are treated as untrusted source text; embedded instructions inside those sources are ignored.

What the result includes:

• qualification - active-framework scores + evidence extracted from this interaction

• qualification_latest - the deal's cumulative health_pct + per-dimension trend

• meddpicc / meddpicc_latest - compatibility aliases used when the active framework is MEDDPICC or legacy records are read

• summary - a 2-3 sentence LLM-generated summary

• customer_themes - customer concerns / selection criteria extracted from this interaction

• scoring_applied - whether this source updated qualification health/customer themes

• source_policy - why this source was treated as confirmed evidence or stored as unconfirmed context

• stage_suggestion - filled only when the content explicitly implies a stage transition (e.g., contract signed -> won, lost deal -> lost); otherwise null

• embedding_stored - whether the similar-deal-search embedding was stored

Source-aware scoring is deliberately conservative:

• direction=inbound defaults to source_confidence=customer_stated, so explicit customer replies, interviews, and meeting notes can update qualification/customer themes.

• direction=outbound defaults to source_confidence=outbound_unconfirmed, so seller-only emails are stored but do not improve health scores.

• interaction_type=internal_note or direction=internal defaults to source_confidence=internal, so internal hypotheses stay out of confirmed scoring.

• direction=mixed is allowed for email threads or calls with both sides represented; only explicit customer statements should be treated as evidence.

The stage never changes automatically. Even if the content says "contract signed," add_interaction does not change the stage directly - it only suggests via stage_suggestion. When Claude asks "shall I move this deal to won?", update_stage makes the actual change after you confirm. This is a deliberate separation to prevent wrong auto-closing.

3. get_deal - view deal details

When to use: To check a specific deal's safe history, qualification scores, and interaction records.

Example:

Show me the full Hyundai Precision deal. deal_id is a3f9...

You get stored interactions, any legacy meeting records, per-interaction qualification extraction, and the cumulative health_pct. Legacy meddpicc_latest fields are still returned for compatibility when present. Raw meeting notes, raw interaction content, contacts, and embeddings are excluded from this normal read path.

Developer surface only: get_deal_raw can intentionally return raw notes, raw interaction content, and contacts for admin/debug inspection, but it requires confirmed_by_user=true, a non-empty reason, and include_raw_content=true. Embeddings are still excluded.

4. update_stage - change the pipeline stage

When to use: When a deal moves to the next stage or the outcome is finalized.

update_stage(deal_id, new_stage, actual_close_date="")

Example:

Move the Hyundai Precision deal to the proposal stage.

When moving to won or lost, you can specify the actual close date as YYYY-MM-DD. Omitted -> the processing day is stored. expected_close_date stays as the forecast, and stage_history.entered_at is the system audit time, kept distinct from the actual close date. Moving a closed deal back to an open stage clears actual_close_date.

Stages (in order):

discovery -> qualification -> proposal -> negotiation -> won / lost / stalled

What the result includes:

• actual_close_date - the real close date for Won/Lost

• days_in_previous_stage - how long it spent in the previous stage

• stuck_threshold_days - the stuck threshold for a new Active stage; otherwise null

• Qualification gaps are recomputed per stage (e.g., under the default MEDDPICC framework, a drop in Identify Pain at the proposal stage is not a gap - it's a positive signal that the pain is being resolved)

5. update_deal - fix an existing deal's amount or confirmed metadata

When to use: When an existing deal's deal_size_status is missing, to save after the user confirms customer-budget / quote / strategic-zero, or to correct confirmed metadata such as company, industry, industry tags, customer segment, and close dates.

This tool stays intentionally narrow. It can update deal-value fields and selected metadata, but it does not change pipeline stage, interactions, meetings, contacts, or raw notes. Stage transitions still go through update_stage.

Example:

The existing ArcanaGames deal has evidence of a closed contract, so save it as quoted.
Note the rationale as "CEO said let's sign today and paid same-day."

Required conditions:

• confirmed_by_user=true

• Value updates require deal_size_note with the user's confirmation rationale or meeting evidence

• Metadata updates require update_note or a fallback deal_size_note

Value edits are logged to deal_value_history; metadata edits are logged to deal_metadata_history. Recognizable mixed industry labels are normalized into primary industry, industry_tags, and customer_segment; unmapped labels should be corrected with an explicit confirmed update.

6. list_deals - see all deals at a glance

When to use: When you want the whole pipeline at a glance. Good for a weekly review.

Example:

Show me all deals, stuck ones first.

Or a specific stage only:

Show me only the deals in the proposal stage.

Result:

• health_pct - overall qualification score (0-100; MEDDPICC by default)

• gaps - list of weak, low-scoring dimensions

• is_stuck - whether the Active-stage dwell time exceeds the per-stage threshold

• is_overdue / overdue_days - whether an Open deal passed its expected close date

• attention_reasons - multiple reasons: stalled, overdue, stuck, at_risk

• days_in_stage - days spent in the current stage

• data_quality - per-deal missing/invalid/estimated fields and overall coverage

• as_of, timezone, generated_at - reporting base date and generation time

Specify as_of="YYYY-MM-DD" to re-run date-based calculations against the same base date. Stuck deals sort to the top.

7. analyze_deal - optional generated BD strategy

When to use: Only when you explicitly want the server-side LLM to draft a BD strategy memo. By default, this is preview-only and does not persist bd_strategy back onto the deal.

For routine deal status, risk, uncertainty, and next-question review, prefer get_deal_review. For "what information are we missing?" use get_deal_gaps. Those read paths are deterministic, LLM-free, and cheaper.

Example:

Generate a BD strategy memo for the Hyundai Precision deal.

The result includes:

• a summary of current qualification health

• concrete responses per weak dimension

• a recommended agenda for the next meeting

When product context has been indexed, analyze_deal may use bounded seller-side snippets to improve product-fit and positioning advice. Same deal/prompt/product-context calls are cached for 10 minutes to avoid repeated LLM spend; force=true bypasses the cache. To persist the generated strategy, call with persist_strategy=true and confirmed_by_user=true. Confirmed persistence stores product-context reference metadata with the generated strategy, not raw product documents. Preview usage is returned in the response only and is not counted by persisted usage reports.

8. get_metrics - current pipeline-health KPIs

When to use: For instant BI questions in Claude/Codex like "how's pipeline health right now'", "how many at-risk deals'", "show pipeline value and health by stage."

This is the default read tool for numeric pipeline answers. Do not use list_deals to hand-calculate KPIs, and do not use get_insights unless the question is about a legacy/special BI pattern such as win/loss comparison or stage velocity.

Supported metric types are pipeline_health and pipeline_trend.

Parameters:

What the result includes:

• kpis: active/open/stalled/terminal count, open value, avg health, coverage, stuck/overdue, attention count

• stage_breakdown: count/value/health/stuck/overdue in canonical stage order

• health_bands: healthy/watch/at_risk/unassessed counts

• attention_reasons: stalled/overdue/stuck/at_risk reason counts and unique attention-deal count

• pipeline_values, win_rate, data_quality, warnings

The BI path uses no LLM and no embedding. Raw notes, contacts, and vectors are excluded from the metric read path.

Example:

Tell me the current pipeline health

Show pipeline health for the proposal stage only

Show stuck/overdue status for IT-industry deals

9. get_deal_gaps - surface the customer intel you're still missing

When to use: When you want to know what you still need to confirm before pursuing, forecasting, or reviewing a deal.

This is not a table-completeness checker. It prioritizes missing or weak information by practical sales impact and forecast trust. It is read-only, uses no LLM, uses no embedding, and excludes raw notes, raw interaction content, contacts, and vectors.

Use this for missing-information questions across the pipeline or for a single deal. Use get_deal_review when the user wants a broader one-deal status/risk review.

Parameters:

What the result includes:

• summary: deal count, gap-deal count, priority counts, gap status/type counts

• deals: prioritized deals with health band, attention reasons, priority score, and gaps

• each gap includes reason, suggested question, and recommended action

Example:

What important customer information are we missing for active deals'

Show high-priority gaps for negotiation deals

For this deal_id, what should I confirm next'

10. export_report - generate a pipeline report

When to use: When you need a file to share or for a meeting, like "make this week's pipeline report."

Use export_report for manager/team meeting reports and narrative pipeline briefings. For chat-only KPI answers, use get_metrics instead. For spreadsheet-ready CSV ledgers, use export_data instead.

Parameters:

What the result includes:

• csv_path, markdown_path: absolute paths of the generated files

• artifacts: report artifact filename, path, encoding

• metrics, warnings, row_count

• briefing, briefing_sections: compact meeting-ready summary text

• host_report_prompt: a safe prompt the host app can use to polish the deterministic data pack into a more natural manager/team report

The deterministic report data pack uses no LLM and no embedding. Host apps may use that data pack to produce more polished meeting prose, but should not change numbers, company names, stages, amounts, health scores, or warning codes.

Example:

Make this week's pipeline report

Export the proposal stage only as a weekly pipeline report

11. export_data - export Excel/CSV-ready deal ledgers

When to use: When the user asks for raw-but-safe CSV data, Excel records, open deal tables, all-deal ledgers, or won/lost postmortem rows.

Use export_data for spreadsheet work. It is not a narrative report tool.

Datasets:

Parameters:

export_data excludes raw notes, raw email/interview/call content, contacts, and embeddings. It writes UTF-8 BOM CSV and guards spreadsheet formula injection.

Example:

Export the open deal ledger as CSV

Create a won/lost CSV for postmortem review

12. get_usage - inspect server-side LLM usage

When to use: When you want to know how much server-side LLM work this MCP has performed, such as token counts, call counts, and safe cost estimates.

This is read-only. It never returns prompts, raw notes, raw emails, API keys, OAuth tokens, or MongoDB URIs. ChatGPT OAuth is shown as subscription-backed with zero incremental API estimate. API-provider costs are estimated only when you configure usage.pricing.

Example:

Show my Recruit AI MCP usage this month.

Show usage since 2026-06-01.

Atlas Charts Dashboard - Weekly Pipeline Review

When you'd rather see it on screen than as CSV/Markdown, use the Atlas Charts dashboard. The dashboard aggregation spec and setup runbook are in docs/atlas-charts.md.

Render command:

~/miniconda3/envs/deal-intel/python.exe -m deal_intel.cli render-atlas-dashboard --as-of 2026-06-09 --output outputs/atlas_charts/weekly_pipeline_review_20260609.json

To paste a single chart into the Atlas Query bar:

~/miniconda3/envs/deal-intel/python.exe -m deal_intel.cli render-atlas-dashboard --as-of 2026-06-09 --chart-id pipeline_kpis

The six managed chart ids are pipeline_kpis, stage_breakdown, health_bands, attention_deals, qualification_gap_distribution, and legacy-compatible meddpicc_gap_distribution.

Cross-check the dashboard numbers:

~/miniconda3/envs/deal-intel/python.exe -m deal_intel.cli crosscheck-weekly-dashboard --as-of 2026-06-09 --output-dir outputs/m3_3_crosscheck

13. get_insights - legacy/special BI analysis

When to use: To aggregate all deal data and spot patterns. Good for monthly reviews and learning win/loss patterns.

Prefer get_metrics for current pipeline-health KPIs. Prefer customer theme tools for customer concerns, decision criteria, and evidence. get_insights remains useful for special BI variants such as win/loss comparison, gap frequency, industry benchmark, and stage velocity.

You can specify as_of; the response includes timezone and a UTC generated_at. These label the current collection snapshot - they don't reconstruct historical document state.

Seven analysis types:

Example:

Show me the whole pipeline overview.

What's the qualification pattern difference between deals we win and deals we lose'

Which dimension is most often missing'

14. search_deals - semantic similar-deal search

When to use: When you want to reference how past deals in similar situations played out. Search in natural language.

Do not use semantic search for frequency/ranking questions such as "what do customers worry about most?" Use get_customer_themes for that. search_deals is for similar-case retrieval in Mongo-backed mode.

Example:

Find deals where the customer struggled with cost reduction.

Show deals with a strong champion and a clear decision structure.

Any deals with a pattern similar to Hyundai Precision'

How it works:

1. Convert the query into a 384-dim vector

2. Compute cosine similarity against every deal's meeting-summary vector

3. Return them sorted by similarity, highest first

What the result includes:

• score - similarity (0-1, higher = more similar)

• deal_stage, health_pct, gaps - the deal's current state

The local embedding model warms up in the background at server start. While it's loading, warming_up: true is returned, so retry after 5 seconds. After 30+ seconds it switches to a stalled error.

15. get_customer_themes - frequency of customer concerns / selection criteria

When to use: To group meeting evidence across deals and see the topics customers worry about most. It counts by unique deal (not by meeting) and returns representative companies and evidence.

Customer themes are intentionally a 3-step workflow:

1. get_customer_themes ranks recurring concerns or decision criteria.

2. get_customer_theme_breakdown compares those themes by stage, primary industry, industry tag, or theme dimension.

3. get_customer_theme_evidence shows privacy-safe snippets for one known theme_key.

For "show me examples" follow up with get_customer_theme_evidence. For stage/industry/tag comparison, use get_customer_theme_breakdown.

Example:

Show the top 5 things customers worried about most across active deals.

Tell me the most frequent themes and evidence in Decision Criteria.

Filters:

• dimension: all, identify_pain, decision_criteria, metrics

• stage: active, all, or an individual deal stage

• industry: primary industry or industry_tags match

• top_k: up to 20

For cross-industry accounts, keep pipeline and forecast metrics on the single primary industry, then use Customer Themes with the industry filter or get_customer_theme_breakdown(group_by="industry_tag") to see semantic industry-tag groupings.

To backfill themes onto existing data, run this first:

~/miniconda3/envs/deal-intel/python.exe -m deal_intel.cli backfill-customer-themes --apply

backfill-customer-themes is a maintenance/migration command for historical meeting records. It may call the configured server-side LLM once per processed meeting, so run the dry-run first, consider --limit, and avoid treating it as normal daily intake. New emails, interviews, calls, and meetings should go through add_interaction.

The versioned Atlas Charts spec is in atlas/charts/customer_themes.v1.json. See docs/atlas-charts.md for the Customer Themes dashboard setup, including the optional pain_by_industry_tag chart.

Recommended workflow

Recruiting-first:

1. New client/search       -> create_client_company + create_position
2. New candidate           -> create_candidate
3. Evidence capture        -> add_recruiting_interaction
4. Client preference       -> add_client_feedback
5. Role-to-candidate match -> recommend_candidates_for_position
6. Candidate-to-role match -> recommend_positions_for_candidate
7. Pipeline KPIs           -> get_recruiting_metrics
8. Local report artifacts  -> export_recruiting_report

Inherited deal workflow:

1. Right after customer evidence -> add_interaction (meeting/email/interview/call)
2. On stage change           -> update_stage
3. One-deal status/risk      -> get_deal_review
4. Before pursuing/forecast  -> get_deal_gaps (what's still missing)
5. Optional strategy memo    -> analyze_deal (LLM-written BD strategy)
6. Weekly review             -> list_deals (find stuck deals)
7. Pipeline KPIs             -> get_metrics pipeline_health
8. Usage / cost check        -> get_usage
9. Monthly retro             -> get_insights compare_won_lost / stage_velocity
10. Reference similar cases  -> search_deals
11. Customer-concern analysis -> get_customer_themes
12. Dashboard                -> Atlas Charts Weekly Pipeline Review

Architecture

Current source of truth:

• MCP server: src/deal_intel/mcp_server.py

• Current registered tool count: see get_tool_catalog or config_doctor

• Detailed contract: docs/baseline.md

• Documentation map: docs/README.md

• User memory samples: user_docs/README.md

[Claude Desktop / Codex - natural-language input]
         | stdio JSON-RPC
         v
[recruit-ai-mcp  FastMCP server]
         |
         |-- LLM Provider
         |     |-- ChatGPT OAuth (default, Plus/Pro subscription)
         |     |-- Anthropic API (optional)
         |     `-- OpenAI API (optional)
         |
         |-- Embedding Provider
         |     `-- sentence-transformers all-MiniLM-L6-v2
         |          -> runs locally / no API key / 384 dims
         |
         `-- Storage
               |-- local_sample  : bundled fixture + local deals/recruiting
               `-- MongoDB Atlas : recruiting collections plus inherited deals

search_deals
  |-- M0 default : reads summary_embedding, computes cosine in Python
  `-- M10+ option : uses the Atlas Vector Search index

Deal document schema (key fields)

{
  "deal_id": "uuid",
  "company": "Hyundai Precision",
  "industry": "Manufacturing",
  "deal_size_amount": 200000000,
  "deal_size_currency": "KRW",
  "deal_stage": "proposal",
  "expected_close_date": "2026-09-30",
  "expected_close_date_source": "user_provided",
  "actual_close_date": null,
  "stage_history": [
    {"stage": "discovery",     "entered_at": "2026-05-01T..."},
    {"stage": "qualification", "entered_at": "2026-05-15T..."},
    {"stage": "proposal",      "entered_at": "2026-06-01T..."}
  ],
  "interactions": [
    {
      "interaction_id": "uuid",
      "meeting_id": "uuid",
      "date": "2026-06-08",
      "interaction_type": "meeting",
      "direction": "inbound",
      "source_confidence": "customer_stated",
      "raw_content": "Met Director Kim. Defect rate 3.2% -> target 1.5%...",
      "summary": "2-3 sentence LLM-generated summary",
      "meddpicc": {
        "metrics":      {"score": 4, "evidence": "~1.5B KRW/yr loss"},
        "identify_pain": {"score": 5, "evidence": "defect rate 3.2%, line urgent"},
        "champion":     {"score": 3, "evidence": "Manager Park in favor"}
      }
    }
  ],
  "meddpicc_latest": {
    "health_pct": 72.4,
    "gaps": ["economic_buyer", "decision_criteria"],
    "metrics":       {"score": 4.0, "trend": "up"},
    "identify_pain": {"score": 5.0, "trend": "flat"},
    "champion":      {"score": 3.0, "trend": "up"}
  },
  "summary_embedding": [0.012, -0.034, ...],
  "created_at": "2026-05-01T...",
  "updated_at": "2026-06-08T..."
}

Module structure

src/deal_intel/
  mcp_server.py         FastMCP entry point
  cli.py                typer CLI (login-chatgpt, backfill-customer-themes,
                        render-atlas-dashboard, crosscheck-weekly-dashboard,
                        smoke-deal-review, smoke-deal-review-audit,
                        smoke-natural-questions)
  _env.py               dotenv + 3-tier config merge
  _context.py           LLM / MongoDB / Embedding process singletons
  providers/
    llm.py              LLMProvider ABC + Anthropic + ChatGPTOAuth + factory
    embedding.py        EmbeddingProvider + SentenceTransformerProvider + factory
  schema/
    recruiting.py       candidate/client/position/submission/feedback models
    recruiting_fit.py   deterministic fit rubric scoring
    recruiting_match.py candidate-position fit signals + feedback adjustment
    recruiting_recommendation.py
                        recommendation run/result builders
    recruiting_metrics.py
                        recruiting pipeline KPI calculator
    meddpicc.py         compute_meddpicc_latest, Deal/Meeting Pydantic models
    customer_themes.py  customer-theme taxonomy, parser, stage-signal validation
  storage/
    mongodb.py          MongoDBClient - recruiting/deal CRUD + aggregation
    recruiting_collections.py
                        recruiting collection contracts and safe projections
  tools/
    recruiting_records.py
                        candidate/client/position/submission/feedback services
    recruiting_recommendations.py
                        position<->candidate recommendation services
    recruiting_metrics.py
                        read-only recruiting KPI service
    export_recruiting_report.py
                        recruiting Markdown/CSV report export
    create_deal.py
    add_interaction.py  canonical interaction intake + qualification extraction
    add_meeting.py      deprecated compatibility alias for meeting interactions
    get_deal.py
    update_stage.py     stage_history logging + qualification recompute
    update_deal.py      edit deal value and limited metadata after user confirmation
    list_deals.py       health_pct / gaps / stuck-flag aggregation
    get_metrics.py      pipeline_health KPIs / stage aggregation / warnings
    get_deal_gaps.py    read-only prioritized sales follow-up gaps
    export_report.py    human-facing pipeline report export
    export_data.py      spreadsheet-ready CSV data export
    get_user_memory.py  constrained user-memory read context
    record_user_memory.py
                        constrained user-memory append tool
    get_insights.py     7 BI queries plus legacy insight query
    get_customer_themes.py
                        aggregates customer concerns by unique deal count
    analyze_deal.py     qualification gap analysis + BD strategy via LLM
    search_deals.py     Python cosine by default / Atlas semantic search optional

How qualification health_pct is computed

MEDDPICC is the default active framework, so the example below uses the bundled MEDDPICC dimensions and weights. Custom frameworks use the same weighted formula with their own enabled dimensions.

health_pct = sum(dim_avg x weight) / sum(5 x weight) x 100

Weights (tunable in config/defaults.yaml):

Stage-aware gap adjustment (applied automatically on update_stage):

• A drop in Identify Pain at the proposal / negotiation stage -> not a gap (signals the pain is being resolved)

• won deals -> no gaps

Health-band configuration:

Defaults are Healthy >=70, Watch >=40, At Risk <40. These classify the level of qualification validation, not win probability, and can be changed in ~/.recruit-ai/config.yaml once you've accumulated operational data.

metrics:
  health_bands:
    healthy_min: 75
    watch_min: 45
  overdue:
    grace_days: 0
  win_rate:
    minimum_closed_sample: 10

The formal definitions of Active/Open/Stalled and unassessed handling are in docs/metrics.md.

FAQ

Q. Can I fork or customize this? Yes. This project is MIT-licensed. You can fork it, customize the storage, model, scoring, reporting, and workflow behavior, and adapt it to your own deal process. Keep the license and attribution notices when redistributing modified versions.

If you are an AI assistant helping customize this repo, prefer small explicit changes, avoid storing secrets in docs, and record meaningful local modifications in docs or user memory so future agents can understand the fork.

Q. Do my meeting notes have to be perfect? No. A rough memo of the essentials is fine. The LLM just skips dimensions with no evidence.

Q. Do Korean meeting notes work? Yes. Mixed English/Korean works too.

Q. Do I need MongoDB at all? Not for sample. You need MongoDB only when you want persistent real recruiting/deal data or Atlas Charts against your own database.

Q. Do I need a paid MongoDB Atlas plan? No. The default full profile works on the free M0 plan today. sample does not need MongoDB at all, and pro is the paid-infra path. search_deals computes with Python cosine on M0. As deal volume grows you can switch to Atlas Vector Search on M10+.

Q. What does Pro add? pro keeps the same MCP tools but switches semantic search to Atlas Vector Search and API-key LLM operation. The vector index spec is versioned at atlas/vector_indexes/deal_summary_vector.v1.json. If Atlas search fails, the server returns a structured error instead of silently falling back; use docs/pro-fallback-errors.md to record repeatable setup failures.

Q. search_deals returns nothing. Right after the server first starts, the local model may still be warming up - retry after 5 seconds. You also need at least one deal with a stored summary_embedding (run add_interaction with scoring-eligible content).

Q. Should I use ChatGPT OAuth, Anthropic, or OpenAI API? For quick personal use, ChatGPT OAuth is attractive if you already subscribe to ChatGPT Plus/Pro. Anthropic and OpenAI API modes are better when you want explicit API-key operation, team billing, or production-style deployment.