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unhcr-refugees-mcp-server

UNHCR asylum decisions

unhcr_get_asylum_decisions
Read-onlyIdempotent

Get asylum decisions per year (2000 to the latest year) by country of origin and/or asylum: recognized as refugees, complementary protection, rejected, and otherwise closed, with UNHCR's Refugee Recognition Rate and Total Protection Rate computed over substantive decisions (otherwise-closed cases excluded). By default all decision levels are summed and each row lists the levels it includes; appeal-stage decisions can concern people already decided at first instance, so split_by decision_level or filter decision_levels to FI for first-instance rates. Cases and persons are never added together.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
limitNoRows returned inline, 1–500 (default 100). Sorting runs over the full result first; a larger result is staged as a dataframe where dataframes are enabled.
stageNoStage the full result as a dataframe even when it fits inline, e.g. to join it with another unhcr_get_* result in unhcr_dataframe_query. Where dataframes are unavailable it is ignored, and the notice says so.
asylumNoCountry of asylum — where people sought or hold protection; for returns, the country they returned from — as ISO3 codes (DEU, TUR), case-insensitive, same rules as origin. At most 50 codes. Omit to sum every asylum country into one row, or set expand to list each.
expandNoList every country, one row each, for a dimension that origin or asylum leaves unfiltered: origin, asylum, or both. Default none, where an unfiltered dimension is summed into one row. Expanding a dimension that origin or asylum already filters is rejected.none
originNoCountry of origin — where people fled from — as ISO3 codes (SYR, AFG), case-insensitive. ISO 3166 alpha-2 codes (SY) are rewritten to ISO3 and echoed in applied_scope. UNHCR's own codes (GFR) are rejected with the ISO3 to pass instead, and country names are rejected: resolve a name with unhcr_list_reference (topic countries, name_contains). Each listed code returns its own rows; codes are never summed together. At most 50 codes. Omit to sum every origin into one row, or set expand to list each.
sort_byNoOrder of the full result before the inline cut. year (default) orders by year, then origin ISO3, then asylum ISO3; a count field orders largest first, nulls last. Rates are not sortable, since rates on small rounded counts would crowd the top.year
year_toNoLast year of the window. When omitted, the window runs to the latest published year (latest_year in the result). A year past the latest published year is clamped to it and the clamp is reported.
split_byNoProcedure dimensions kept as separate rows: authority (government, UNHCR, or joint) and decision_level (first instance, administrative review, …). Every dimension left out is summed. Default [] sums all authorities and levels, as UNHCR does for its rates. Unit is always kept separate.
year_fromNoFirst year of the window. When omitted, the window starts at the dataset's first year. A year before the dataset's first year is clamped to it and the clamp is reported; a window entirely outside the dataset's years is rejected.
decision_levelsNoKeep only these decision levels before summing, case-insensitive: NA new applications, FI first instance, AR administrative review, RA repeat/reopened, IN US Citizenship and Immigration Services, EO US Executive Office for Immigration Review, JR judicial review, SP subsidiary protection, FA first instance and appeal, TP temporary protection, TA temporary asylum, BL backlog, TR temporary leave to remain, CA cantonal regulations (Switzerland). ["FI"] gives first-instance decisions. Omit for every level.

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
capNoThe limit that was applied.
rowsNoInline rows, sorted, up to limit.
errorNoPresent when the call failed. Absent on success.
shownNoRows returned inline.
noticeNoGuidance for this result: empty-result hints, year clamps, where the full set was staged, or how to narrow.
datasetNoPresent when the full result was staged as a dataframe; read its columns with unhcr_dataframe_describe, then query it with unhcr_dataframe_query.
measureNostock: people in a situation on 31 December; flow: events during the year.
completeNoFalse when the server row cap stopped the upstream fetch; the notice says how many upstream rows were fetched and how to narrow.
truncatedNoTrue when rows were cut at limit.
data_notesNoHow to read the counts: stock or flow, rounding, the meaning of null, caveats.
total_rowsNoRows in the full result before the inline cut. When complete is false, the result is built from only the upstream rows fetched before the cap, so rows and summed counts can fall short of the complete result.
attributionNoAttribution UNHCR requires when these figures are reused.
latest_yearNoNewest year this dataset publishes.
applied_scopeNoThe scope actually sent upstream, including clamped years and code rewrites.

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observed

TDQS

A4.4/5.0
Behavior4/5

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

Annotations already cover read-only/idempotent/openWorld, so the bar is lower, yet the description adds real behavioral context: rates are computed over substantive decisions with otherwise-closed cases excluded, defaults sum all decision levels, and 'cases and persons are never added together.' It does not touch staging/pagination behavior, which the schema handles.

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?

Front-loaded with what is retrieved before the caveats, and every clause carries information (rate definitions, appeal-stage caveat, cases-vs-persons rule). It is dense and reads as one long paragraph, but nothing is redundant with structured fields.

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?

For a 10-parameter analytical query tool with an output schema and full schema coverage, the description supplies the conceptual layer an agent needs: metric definition, exclusion rule, default aggregation, and the decision-level caveat. Nothing essential 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 100%, so the baseline is 3, but the description lifts key parameters out of the schema by explaining the consequence of defaults ('all decision levels are summed and each row lists the levels it includes') and linking split_by/decision_levels to first-instance rates. That is meaning beyond raw field documentation.

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?

States a specific verb and resource (asylum decisions per year by origin and/or asylum country) and enumerates the outcome categories returned, so an agent can distinguish it from unhcr_get_asylum_applications without opening the schema. The scope of what is retrieved is unambiguous.

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?

Gives clear operational guidance: default summing of decision levels, and the instruction to 'split_by decision_level or filter decision_levels to FI for first-instance rates' when appeal-stage cases would contaminate the result. It stops short of naming sibling tools (e.g., unhcr_get_asylum_applications) as alternatives or stating when not to use this tool.

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

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