FRED (economic data)
PYPI · MCPWRIGHT-FRED
FRED economic time series + ALFRED vintages: data as originally published, before revisions.
Available components
Last scanned (9 hours ago)
Scores higher than 83% of PyPI servers
Category Finance and Payments
How this component scores in each security and reliability category. Every signal is checked automatically from public evidence about the published package, including repeated runs of it in an isolated sandbox, and we only credit what we can confirm. How we score → Why this is hard to score →
Supply Chain Security100
- No malware found by supply-chain analysis.Pass
- No known CVEs affecting this package version or its production dependencies.Pass
- Runs uv_build at install time, a recognised build step with no custom scripting around it. View diagnostics → Pass
- 0 of 29 dependencies flagged as unhealthy. View diagnostics → Pass
Provenance & Transparency45
- Source repository is publicly reachable at the declared URL. View diagnostics → Pass
- Provenance check failed: no build-provenance attestation is published. See how to fix → View diagnostics → Fail
- Clear OSI-approved license (MIT).Pass
- Actively maintained (last published 76 days ago).Pass
- Disclosure check failed: no security disclosure policy was found in the source repository. See how to fix → Fail
Schema Quality & AI Usability69
- AI-judged instruction clarity (excellent).Pass
- Context-footprint check failed: tool/resource definitions use about 1625 tokens (~180/item across 9 items; 9 tools + 0 resources), over budget; trim descriptions and params. See how to fix → Fail
- Usage-examples check failed: none of the tools include examples. See how to fix → Fail
Stability & Change Management100
- No destabilizing schema changes in the last 30 days.Pass
Tool Coverage71
- 100% of tools have a non-trivial description (not blank, and not just the tool's name).Pass
- 0% of tool parameters carry a description.Fail
- Structured output schemas are declared (100% of tools); any adoption earns full credit.Pass
Tool Safety100
- No prompt-injection markers were found in the server instructions, tool names or descriptions we captured.Pass
- We read all 9 captured tool definition(s), and no name or description among them implies an irreversible operation.Pass
- An AI judge read all 10 captured unit(s) of tool text and found none that tries to manipulate the model reading it.Pass
Capabilities100
- Implements a supported MCP spec version (2025-11-25); the latest is 2026-07-28.Pass
Checks we ran (16)
- Malware scan
- Known CVEs
- Install scripts
- Dependency health
- Source repository
- Provenance
- License
- Maintenance
- Security disclosure
- Schema quality
- Stability
- Tool coverage
- Injection markers
- Destructive annotations
- Judged manipulation
- MCP protocol
Does not apply to this package (2)
- Endpoint SecurityScored on hosted endpoints only. A package has no live endpoint of its own for us to probe.
- Transport & ReachabilityScored on hosted endpoints only. A package has no live endpoint of its own for us to probe.
The score does not measure everything. What the score does not measure →
Every change we have recorded for this component, newest first. Security-relevant changes are always shown. ▲ marks a change for the better, ▼ a change for the worse; unmarked changes are neutral.
- 5 Nov 26 +15
- Malware scan: unverified → pass ▲ security
- 4 Nov 26 0
- Security disclosure: unverified → fail ▼ functional
- 3 Nov 26 −15
- Malware scan: pass → unverified ▼ security
- 2 Nov 26 0
- Security disclosure: fail → unverified ▼ functional
- 1 Nov 26 0
- Stability: 0.97 → pass security
- 31 Oct 26 +16
- Malware scan: unverified → pass ▲ security
- Security disclosure: unverified → fail ▼ functional
- 30 Oct 26 −16
- Malware scan: pass → unverified ▼ security
- Security disclosure: fail → unverified ▼ functional
- Stability: pass → 0.93 functional
- 24 Oct 26 0
- Stability: 0.97 → pass security
Diagnostic detail from the automated scan of this channel: what the scanner observed at each step, so you can see exactly where a check passed or failed. It is informational only and never changes the trust score.
Captured 5 Nov 2026 · Analysed pypi/mcpwright-fred@0.1.1
Provenance No attestation
The registry publishes no build provenance for this version, so there is nothing to verify.
| Result | No attestation |
|---|---|
| Ecosystem | pypi |
| Reason | No attestation published |
Background: How many MCP packages publish verified provenance →
Install scripts 1 script
| Hook | Tier | Command |
|---|---|---|
| build_backend | Known build tool | uv_build |
Background: Why install scripts are a supply-chain risk →
Dependencies 29 packages
| Packages resolved | 29 |
|---|---|
| Tree resolution | Complete |
Background: SBOMs and build attestations, explained →
The tools this component advertises to a client, with an estimated token cost for each. Expand a tool to see its parameters and schema. The per-tool counts are indicative and are not scored directly; the schema's total context footprint is one signal in Schema Quality & AI Usability. A tool's description is untrusted text the model reads on every call, which is what makes this list a security surface and not just an inventory: how tool poisoning works →
compare_series Compare series ~166
Align 2-5 series on one date axis for comparison. `series_ids`: 2-5 FRED series IDs. Same `start`/`end`/`transform` as `get_observations`. If the series have DIFFERENT native frequencies you must pass `frequency` (a frequency at least as coarse as the coarsest series) — mixing frequencies without it returns misaligned dates. Units differ per series unless a relative transform (e.g. pct_change_yoy) is used — check each entry's `units` before comparing levels.
| Name | Type | Req | Description |
|---|---|---|---|
| end | – | – | – |
| frequency | – | – | – |
| max_points | integer | – | – |
| series_ids | array | yes | – |
| start | – | – | – |
| transform | string | – | – |
| Name | Type | Req | Description |
|---|---|---|---|
| rows | array | yes | Aligned values, oldest first |
| series | array | yes | The series compared |
| transform | string | yes | The transform applied to every series |
| truncated | boolean | yes | True if more rows exist than were returned |
No examples provided.
get_latest Get latest value ~108
The most recent value of a series, plus its next scheduled release. `series_id`: a FRED series ID. The value is the most recent NON-MISSING print (daily series publish '.' on holidays) and is CURRENT data — it may itself be revised later (see `get_revision_history` for how much this series typically moves). `next_release_date` is null when FRED publishes no schedule for the series' release.
| Name | Type | Req | Description |
|---|---|---|---|
| series_id | string | yes | – |
| Name | Type | Req | Description |
|---|---|---|---|
| date | string | yes | Date of the latest observation (period start) |
| last_updated | string | yes | When FRED last updated this series |
| next_release_date | – | – | The next scheduled release date for this series' release, if FRED publishes a schedule for it |
| series_id | string | yes | The FRED series ID |
| title | string | yes | Series title |
| units | string | yes | Units of the value |
| value | – | yes | The latest value (null if missing) |
No examples provided.
get_observations Get observations ~211
A series' values in a date window, optionally transformed/downsampled. `series_id`: a FRED series ID. `start`/`end`: ISO dates bounding the window. `transform`: level | change | pct_change | pct_change_yoy | log. `frequency`: optionally downsample to daily/weekly/monthly/quarterly/ semiannual/annual (with `aggregation` avg | sum | eop); only coarser than the native frequency is valid. `max_points` caps the result, keeping the most RECENT points (`truncated` tells you when the cap bit). Values are CURRENT (post-revision) data. For numbers as originally published, use `get_series_as_of` / `get_revision_history`.
| Name | Type | Req | Description |
|---|---|---|---|
| aggregation | string | – | – |
| end | – | – | – |
| frequency | – | – | – |
| max_points | integer | – | – |
| series_id | string | yes | – |
| start | – | – | – |
| transform | string | – | – |
| Name | Type | Req | Description |
|---|---|---|---|
| frequency | string | yes | Frequency of the values returned |
| observations | array | yes | The data points, oldest first |
| series_id | string | yes | The FRED series ID |
| title | string | yes | Series title |
| transform | string | yes | The transform applied (level if none) |
| truncated | boolean | yes | True if more observations exist than were returned — narrow the date range or raise max_points for more |
| units | string | yes | Units of the VALUES RETURNED — the native units when transform='level', otherwise the transform (e.g. '% change from year ago') |
No examples provided.
get_release_calendar Get release calendar ~81
Upcoming data-release dates ("when is the next jobs report / CPI?"). `days`: how far ahead to look (1-90, default 14). Returns each release's name and date, soonest first. Dates are the SCHEDULED dates FRED knows about; not every release publishes a schedule.
| Name | Type | Req | Description |
|---|---|---|---|
| days | integer | – | – |
| Name | Type | Req | Description |
|---|---|---|---|
| end | string | yes | Window end (YYYY-MM-DD) |
| releases | array | yes | Release dates in the window, soonest first |
| start | string | yes | Window start (YYYY-MM-DD) |
| truncated | boolean | – | True if more release dates exist in the window than were returned — an absent release is NOT proof nothing is scheduled; narrow `days` |
No examples provided.
get_revision_history Get a data point's revision history ~169
One data point's life across revisions: earliest archived print -> today. `series_id`: a FRED series ID. `observation_date`: the data point's PERIOD START date (ISO) — quarterly series use quarter starts (Q4 2008 = "2008-10-01"), monthly use month starts. Returns the earliest archived value, every revision with its publication date, the current value, and the total drift. Caveat: ALFRED's archive starts late for many series (`archive_starts` shows where). `initial_value` is the true first print — what decision-makers actually saw — only when the archive reaches back to the observation's original release.
| Name | Type | Req | Description |
|---|---|---|---|
| observation_date | string | yes | – |
| series_id | string | yes | – |
| Name | Type | Req | Description |
|---|---|---|---|
| archive_starts | – | – | The first vintage in ALFRED's archive for this point. If this is much later than observation_date, initial_value is the earliest ARCHIVED value, not the true first print |
| current_value | – | yes | The value as published today, after all revisions |
| initial_value | – | yes | The value in ALFRED's earliest archived vintage — the as-published 'real-time' number when archive_starts reaches back to the observation's release |
| observation_date | string | yes | The observation the history is for (period start date) |
| series_id | string | yes | The FRED series ID |
| steps | array | yes | Each distinct value the point has held, oldest first (consecutive re-publications of an unchanged value are merged) |
| steps_truncated | boolean | – | True if middle revisions were omitted to cap the list (the initial and current steps are always kept) |
| title | string | yes | Series title |
| total_revision | – | – | current_value - initial_value (null if either is missing) |
| units | string | yes | Units of the values |
No examples provided.
get_series Get series metadata ~84
Full metadata for one series: units, frequency, range, and notes. `series_id`: a FRED series ID (e.g. "GDPC1"). The `notes` are returned verbatim — they carry methodology caveats and, for third-party series, the owner's copyright terms (also flagged via `copyrighted`).
| Name | Type | Req | Description |
|---|---|---|---|
| series_id | string | yes | – |
| Name | Type | Req | Description |
|---|---|---|---|
| copyrighted | boolean | yes | True if third-party-copyrighted (see notes for the owner's terms) |
| frequency | string | yes | Native frequency |
| last_updated | string | yes | When the series data was last updated |
| notes | – | – | The series' FRED notes, verbatim — methodology, caveats, and any copyright notice (never stripped) |
| observation_end | string | yes | Latest observation date (YYYY-MM-DD) |
| observation_start | string | yes | First observation date (YYYY-MM-DD) |
| popularity | – | – | FRED popularity rank component |
| seasonal_adjustment | string | yes | Seasonal adjustment |
| series_id | string | yes | The FRED series ID |
| title | string | yes | Series title |
| units | string | yes | Units of the raw data |
No examples provided.
get_series_as_of Get series as of a date (vintage) ~155
A series EXACTLY as it was known on a past date — before later revisions. `series_id`: a FRED series ID. `as_of`: the knowledge date (ISO) — e.g. "2009-03-18" shows the data the Fed saw at its March 2009 meeting. `start`/`end` bound the observation window as usual. Vintage coverage varies by series: dates before the first vintage have no data (check `get_vintage_dates`). Values can differ sharply from today's — that's the point.
| Name | Type | Req | Description |
|---|---|---|---|
| as_of | string | yes | – |
| end | – | – | – |
| max_points | integer | – | – |
| series_id | string | yes | – |
| start | – | – | – |
| Name | Type | Req | Description |
|---|---|---|---|
| as_of | string | yes | The knowledge date — values are as published on this date, before any later revisions |
| observations | array | yes | The data as known on as_of, oldest first |
| series_id | string | yes | The FRED series ID |
| title | string | yes | Series title (current title) |
| truncated | boolean | yes | True if more observations exist than were returned |
| units | string | yes | Units of the values |
No examples provided.
get_vintage_dates Get vintage dates ~98
When a series' data was released or revised (its ALFRED vintages). `series_id`: a FRED series ID. Answers "how often is this revised?" and bounds the vintage tools: `get_series_as_of` has no data before `first_vintage`. The full list is capped to the most recent dates; `total_vintages` is the true count.
| Name | Type | Req | Description |
|---|---|---|---|
| series_id | string | yes | – |
| Name | Type | Req | Description |
|---|---|---|---|
| first_vintage | – | – | The earliest vintage date — as-of queries before this date have no data |
| latest_vintage | – | – | The most recent vintage date |
| series_id | string | yes | The FRED series ID |
| total_vintages | integer | yes | Total number of vintage dates on record |
| truncated | boolean | yes | True if total_vintages exceeds the dates listed |
| vintage_dates | array | yes | Vintage dates, most recent first (capped — see truncated) |
No examples provided.
search_series Search for series ~112
Find FRED series by free-text search, best matches first. `query`: plain words (e.g. "unemployment rate", "median home price"). Returns series IDs with title, units, native frequency, and a `copyrighted` flag — some FRED series are owned by third parties whose terms (in the series notes) apply beyond personal use. Resolve a series ID here first; the data tools key off it.
| Name | Type | Req | Description |
|---|---|---|---|
| limit | integer | – | – |
| query | string | yes | – |
| Name | Type | Req | Description |
|---|---|---|---|
| result | array | yes | – |
No examples provided.
How do I install the FRED (economic data) MCP server?
FRED (economic data) runs locally as a PyPI package, launched with uvx mcpwright-fred. Ready-made configuration for Claude, Cursor, VS Code, Codex and 5 more is on this page, copied from each client's own documentation.
pypi · mcpwright-fred
claude mcp add mcpwright-fred-mcp -- uvx mcpwright-fred
What is the FRED (economic data) MCP server?
FRED (economic data) is an MCP server listed in the public MCP registry as io.github.mcpwright/fred-mcp. FRED economic time series + ALFRED vintages: data as originally published, before revisions. This page covers its PyPI package (mcpwright-fred).
Is the FRED (economic data) MCP server safe to use?
FRED (economic data) scores 81 out of 100 on VerifyMCP. We found no known CVEs affecting it as of 5 November 2026. That is a record of what we were able to check automatically, not an endorsement. The category breakdown on this page shows every signal behind the number, including the ones we could not confirm.
What tools does the FRED (economic data) MCP server expose?
FRED (economic data) exposes 9 tools: search_series, get_series, get_observations, get_latest, compare_series, and 4 more. Their descriptions and schemas cost roughly 1,184 tokens of context every time the server is loaded.
Is the FRED (economic data) MCP server still maintained?
FRED (economic data) is still listed as active in the MCP registry. We last reached this channel on 5 November 2026. Those dates come from our own scans of the registry and the channel itself, not from anything the publisher announced.
What licence is the FRED (economic data) MCP server under?
FRED (economic data) declares the MIT licence, which is OSI-approved. That covers the source only, and says nothing about the cost of any service it calls.
What did VerifyMCP test on the FRED (economic data) MCP server?
VerifyMCP ran 16 checks on FRED (economic data), covering Malware scan, Known CVEs, Install scripts, Dependency health and 12 more. Endpoint Security and Transport & Reachability do not apply to a PyPI package, so they were not tested. The score does not measure everything, and the scoring documentation lists what it leaves out.