# Précis Finance MCP (remote · mcp.precis.finance)

Public read-only Précis Finance MCP demo with synthetic data; no account or credentials required.

- Trust score: 54/100 (low)
- Change this week: +6
- Registry status: active
- Liveness: live
- Owner verified: no
- Last scored: 2026-08-03

## Components

- remote · `mcp.precis.finance`: 54/100 (this document), [markdown](https://verifymcp.io/servers/precis-finance-precis-finance-mcp/mcp.md), [page](https://verifymcp.io/servers/precis-finance-precis-finance-mcp/mcp)

## Channel facts

- Endpoint: `https://mcp.precis.finance/mcp`
- Transports: `streamable-http`
- Auth: `none`
- Version: `0.2.4`

## Trust breakdown

How this component scores in each security and reliability category. Every signal is checked automatically against the live server, and we only credit what we can confirm. Scores are 0–100 per category. Scoring method: https://verifymcp.io/docs/scoring (what has changed: https://verifymcp.io/docs/scoring/changelog)

Scored 2026-08-03.

- **Endpoint Security**: 57/100
  - The endpoint's TLS certificate is valid, in date, and uses a strong key.
  - Authorisation not fully verified: no authorisation is required to call this server, and 17 tool(s) never declared a destructiveHint. The MCP spec treats an absent hint as destructive by default, so we cannot call this surface safe.
  - HTTPS is enforced; there's no plaintext access path.
  - HSTS check failed: the Strict-Transport-Security header is absent.
  - DNSSEC check failed: this domain isn't protected by DNSSEC.
- **Transport & Reachability**: 100/100
  - Verified streamable-http transport via a live MCP handshake.
- **Schema Quality & AI Usability**: 34/100
  - 0% of prompts and resources have a non-trivial description (not blank, and not just the item's name).
  - AI-judged instruction clarity (good).
  - Context-footprint check failed: tool/resource definitions use about 5774 tokens (~303/item across 19 items; 17 tools + 2 resources), over budget; trim descriptions and params.
  - Usage-examples check failed: none of the tools include examples.
- **Stability & Change Management**: 27/100
  - Stability observed for 8 of 30 days with no destabilising changes; credit accrues until the full window elapses.
- **Tool Coverage**: 67/100
  - 100% of tools have a non-trivial description (not blank, and not just the tool's name).
  - 0% of tool parameters carry a description.
- **Capabilities**: 73/100
  - Spec-recency check failed: implements MCP spec 2025-06-18; the latest is 2026-07-28.
  - Supports UI / widget rendering.

## Install

### Claude

```bash
claude mcp add --transport http precis-finance-precis-finance-mcp https://mcp.precis.finance/mcp
```

### Codex

```toml
[mcp_servers.precis-finance-precis-finance-mcp]
url = "https://mcp.precis.finance/mcp"
```

### opencode

```json
{
  "$schema": "https://opencode.ai/config.json",
  "mcp": {
    "precis-finance-precis-finance-mcp": {
      "type": "remote",
      "url": "https://mcp.precis.finance/mcp",
      "enabled": true
    }
  }
}
```

### OpenClaw

```bash
openclaw mcp add precis-finance-precis-finance-mcp --url https://mcp.precis.finance/mcp --transport streamable-http
```

### Hermes

```yaml
mcp_servers:
  precis-finance-precis-finance-mcp:
    url: "https://mcp.precis.finance/mcp"
```

### Other

```json
{
  "mcpServers": {
    "precis-finance-precis-finance-mcp": {
      "type": "http",
      "url": "https://mcp.precis.finance/mcp"
    }
  }
}
```

The mcpServers block is a cross-client convention. Remote transports vary, so check your client's docs.

## Changelog

Every change recorded for this component, newest first. Days that predate change tracking, or that we cannot explain, say so: "we were watching and nothing happened" and "we were not watching" are different claims.

### 2026-08-03 (score 54, +1)

No change was recorded against any check on this day. Stability & Change Management went from 23 to 27. That category is still filling its 30-day observation window: 7 days of observed history at the previous scan, 8 at this one. The score rises as the window fills, whether or not the server changes.

### 2026-07-31 (score 53, +3)

- [functional] We updated how we score, so this day's move reflects our rubric, not a change to the server

### 2026-07-29 (score 50, +1)

No change was recorded against any check on this day. Stability & Change Management went from 7 to 10. That category is still filling its 30-day observation window: 2 days of observed history at the previous scan, 3 at this one. The score rises as the window fills, whether or not the server changes.

### 2026-07-28 (score 49, +1)

No change was recorded against any check on this day. Stability & Change Management went from 3 to 7. That category is still filling its 30-day observation window: 1 days of observed history at the previous scan, 2 at this one. The score rises as the window fills, whether or not the server changes.

### 2026-07-27 (score 48, 0)

- [functional] We updated how we score, so this day's move reflects our rubric, not a change to the server

### 2026-07-26 (score 48)

First indexed and scored.

## MCP tools (17)

### `precis_orientation` (~53 tokens)

Call this first. Returns how to use Précis over this connector: the data model (scenarios, metrics, statements, dimensions), the reporting-tool variants, and how to build charts. Read it before composing queries.

### `list_scenarios` (~19 tokens)

List the available planning scenarios and their status.

### `list_kpis` (~28 tokens)

Browse the metric catalogue — metric keys, formats, domains, and the dimensions available per metric.

### `list_inspection_sources` (~19 tokens)

List the row-level sources available for inspection.

### `get_inspection_schema` (~27 tokens)

Get the column schema for an inspection source.

Input parameters:

- `source_key` (string, required)

### `inspect_rows` (~90 tokens)

Inspect the row-level detail behind a figure, from an enabled inspection source. Returns a capped sample for reasoning plus a grid for the user.

Input parameters:

- `columns`
- `filename`
- `filters`
- `limit`
- `period_end`
- `period_start`
- `scenario_id` (string, required)
- `sheet_name`
- `source_key` (string, required)

### `run_statement` (~183 tokens)

Run a financial statement — P&L, variance report, or executive summary. Rows are statement lines (Revenue, Direct Cost, Gross Margin, …); columns are scenarios. Supports an optional dimension breakdown (e.g. by period or cost centre). For an unspecified general P&L, prefer `full_pnl` when it is listed by precis_orientation. Give every scenario a concise, user-facing `alias` such as Actuals, Budget, Variance, or Var %. Shows the user a formatted table.

Input parameters:

- `decimals`
- `dimensions`
- `filename`
- `filters`
- `layout` (string)
- `overwrite` (boolean)
- `period_end`
- `period_start`
- `scale`
- `scenarios`
- `sheet_name`
- `statement`
- `target`

### `run_statement_data` (~217 tokens)

Run a financial statement — P&L, variance report, or executive summary. Rows are statement lines (Revenue, Direct Cost, Gross Margin, …); columns are scenarios. Supports an optional dimension breakdown (e.g. by period or cost centre). For an unspecified general P&L, prefer `full_pnl` when it is listed by precis_orientation. Give every scenario a concise, user-facing `alias` such as Actuals, Budget, Variance, or Var %. Returns the raw figures (and a `data_ref`) for your own analysis or to build a chart — pass the `data_ref` to eval_chart_transform. Does not show the user a table.

Input parameters:

- `decimals`
- `dimensions`
- `filename`
- `filters`
- `layout` (string)
- `overwrite` (boolean)
- `period_end`
- `period_start`
- `scale`
- `scenarios`
- `sheet_name`
- `statement`
- `target`

### `run_metric` (~158 tokens)

Break one or more metrics down by a dimension — revenue by project, utilisation by employee, headcount trends, GL account drill-down. Rows are the dimension; columns are metrics × scenarios. Pass `scenarios` explicitly and give every scenario a concise, user-facing `alias` such as Actuals, Budget, Variance, or Var %. Shows the user a formatted table.

Input parameters:

- `decimals`
- `dimensions`
- `filename`
- `filters`
- `layout` (string)
- `metrics` (array, required)
- `overwrite` (boolean)
- `period_end`
- `period_start`
- `scale`
- `scenarios`
- `sheet_name`
- `target`

### `run_metric_data` (~192 tokens)

Break one or more metrics down by a dimension — revenue by project, utilisation by employee, headcount trends, GL account drill-down. Rows are the dimension; columns are metrics × scenarios. Pass `scenarios` explicitly and give every scenario a concise, user-facing `alias` such as Actuals, Budget, Variance, or Var %. Returns the raw figures (and a `data_ref`) for your own analysis or to build a chart — pass the `data_ref` to eval_chart_transform. Does not show the user a table.

Input parameters:

- `decimals`
- `dimensions`
- `filename`
- `filters`
- `layout` (string)
- `metrics` (array, required)
- `overwrite` (boolean)
- `period_end`
- `period_start`
- `scale`
- `scenarios`
- `sheet_name`
- `target`

### `search_hierarchy` (~49 tokens)

Search the dimension hierarchies (cost centres, accounts, …) to find valid codes and ids before composing a query.

Input parameters:

- `dimension`
- `limit`
- `query`

### `list_dimensions` (~46 tokens)

List the dimensions defined in the model — keys, labels, and kinds (leaf / derived / ragged hierarchy). Catalogue metadata only; use search_hierarchy to list a dimension's members.

### `list_variants` (~26 tokens)

List the what-if variants of a scenario.

Input parameters:

- `scenario_id` (string, required)

### `list_load_history` (~74 tokens)

List data-load attempts from the ingestion audit trail — when each dataset landed, with what status. Answers "is April in yet?" / "when was this data last loaded?".

Input parameters:

- `binding_id`
- `dataset_id`
- `limit` (integer)
- `period`
- `status`

### `get_load_status` (~40 tokens)

Fetch one data load's full detail by load_id — timestamps, status, rows landed, and any error message.

Input parameters:

- `load_id` (string, required)

### `list_bindings` (~44 tokens)

List the configured data feeds (ingestion bindings) with their schedule — which datasets load, from where, how often.

Input parameters:

- `source_id`
- `target`

### `get_binding` (~35 tokens)

Fetch one data feed's full configuration: source, target dataset, schedule, and extract parameters.

Input parameters:

- `binding_id` (string, required)

## Diagnostics

Captured diagnostic sections: TLS, DNSSEC, Authorisation, Transports. The full working is on the page: https://verifymcp.io/servers/precis-finance-precis-finance-mcp/mcp#diagnostics

## Score history

- 2026-08-03: 54
- 2026-08-02: 53
- 2026-08-01: 53
- 2026-07-31: 53
- 2026-07-30: 50
- 2026-07-29: 50
- 2026-07-28: 49
- 2026-07-27: 48
- 2026-07-26: 48

## Links

- Remote endpoint: https://mcp.precis.finance/mcp
- Repository: https://github.com/precis-finance/precis-finance-mcp
- Website: https://precis.finance/mcp
- Changelog RSS feed: https://verifymcp.io/servers/precis-finance-precis-finance-mcp/mcp/changelog.xml
- Changelog JSON feed: https://verifymcp.io/servers/precis-finance-precis-finance-mcp/mcp/changelog.json
- HTML version of this page: https://verifymcp.io/servers/precis-finance-precis-finance-mcp/mcp