factiq

Get series data

get_series

Fetch one series' data: timeseries, tabular, or COMPOUND:: all work.

Returns title, units, frequency, notes and data points, with source metadata. transform="yoy_pct" (percent change) or "yoy_diff" (difference, for rates) adds a column with the change versus the same period one year earlier, matched by calendar date — use it for inflation or growth from one series instead of a LAG(value, 12) query, which goes wrong after any missing month. The cell is null where the prior-year period is absent, and transform.periods_without_prior lists those periods. If the result carries a coverage_note, the series has periods with no observation (listed in missing_periods): say so in the answer, leave them blank rather than interpolating, and label any aggregate that spans them as partial. SEC-backed results include row_sources keyed by result_index; each entry identifies the supporting filing, accession/form/publication date, whether the observation is reported or derived, and its standardized source_link. Cite that row's source URL beside any figure and label derived observations as derived. A document-precision link does not open an exact passage. IMF (imf schema) results also carry projections_start_after (the last year the IMF counts as actual; later years are IMF staff projections), release_label (the IMF publication, e.g. "Fiscal Monitor (October 2026)") and, when either is known, a projection_note. Say which years are projections when you use them. Both fields are null when the IMF gives no such fact for the series. At most 50 points come back; if the series is longer the result is flagged "truncated": true — narrow it with from_year/to_year, or pull the series through run_sql with an aggregation (e.g. monthly/quarterly via date_trunc) rather than trying to fetch every point.

Input Schema

TableJSON Schema

Name	Required	Description	Default
`schema`	Yes
`to_year`	No
`from_year`	No
`series_id`	Yes
`transform`	No

Output Schema

TableJSON Schema

Name	Required	Description	Default
No arguments

Schema Changelog

Changes observed during successful MCP inspections.

First observedOct 16, 2026

TDQS

A3.8/5.0

Behavior1/5

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

The description describes a pure read ('Fetch one series' data', returns points, adds columns, flags truncation), but the annotations declare readOnlyHint=false and idempotentHint=false, i.e. a non-read-only, non-idempotent operation. That is a direct conflict between the two sources of truth, and the rubric assigns 1 for a contradiction regardless of the otherwise rich behavioural detail.

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?

Long, but front-loaded with the core fetch semantics and each subsequent block (transform semantics, coverage notes, SEC row sources, IMF projections, truncation) is operational rather than filler. Still dense enough that an agent must read several sentences before the truncation limit and its workaround appear.

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?

Though an output schema exists, the description goes further and explains the fields that matter for correct downstream use: `coverage_note`/`missing_periods`, `row_sources`/`result_index` citation duties, IMF `projections_start_after`/`release_label`, and the 50-point truncation flag. Nothing an agent needs in order to call it correctly is missing.

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 description coverage is 0%, so the description has to carry the load and largely does: it enumerates the legitimate `transform` values (`yoy_pct`, `yoy_diff`), explains what each computes and how prior periods are matched, and gives the purpose of `from_year`/`to_year` (narrowing a truncated series). It adds nothing for the two required params (`schema`, `series_id`), which is the remaining gap.

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?

Opens with a specific verb+resource ('Fetch one series' data') and immediately scopes what formats are supported (timeseries, tabular, COMPOUND). Between 'fetch one series' vs 'search_series' vs pulling via 'run_sql', the agent can tell this apart from every sibling without opening a schema.

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 routes around alternatives: use transform instead of a `LAG(value, 12)` query, and if the result is truncated use `from_year`/`to_year` or pull through `run_sql` with an aggregation. It never states when to choose `get_series` over `search_series` (discovery vs retrieval), so it is clear context but not a full decision rule.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

All factiq tools Inspect

Try in Browser

One endpoint for every MCP toolRayrun runs every MCP server behind one URL and holds the credentials. Each call is decided per client, tool, and argument, then recorded.ray.runAd

Glama MCP Gateway

Add one secure layer between your agents and this server.

Resources

Need Help?