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Glama

Unicef

by io.github.pipeworx-io

Get Data

get_data
Read-onlyIdempotent

Pull observations from a UNICEF dataset. key is a dot-separated SDMX dimension filter, one position per dimension in the order given by dataflow_structure; leave a position empty to wildcard it. Call dataflow_structure first to learn the dimension order and valid codes. Example: get_data({ dataflow_id: "CME", key: ".CME_MRY0T4.." }) filters by INDICATOR=CME_MRY0T4 (under-five mortality rate) and wildcards REF_AREA/SEX/WEALTH_QUINTILE. Omit key (or pass "") to fetch all series — can be large. Returns decoded series with their dimension labels and per-period values.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
keyNoDot-separated dimension filter (one position per dimension, empty = wildcard), e.g. ".CME_MRY0T4..". Omit for all series.
last_nNoReturn only the last N observations per series.
end_periodNoEnd period, e.g. "2022".
max_seriesNoCap the number of decoded series returned (default 200).
dataflow_idYesUNICEF dataflow id, e.g. "CME". The agency,flow,version reference is built automatically.
start_periodNoStart period, e.g. "2010".

Schema Changelog

Changes observed during successful MCP inspections. Dates show when Glama detected each change.

  1. Changed1 schema field changed
    • addedInput schema / examples
      Added value: +[
+  {
+    "dataflow_id": "CME",
+    "end_period": "2022",
+    "key": ".CME_MRY0T4..",
+    "start_period": "2010"
+  },
+  {
+    "dataflow_id": "NUTRITION",
+    "key": "",
+    "last_n": 5
+  }
+]
  2. First observed

TDQS

A4.5/5.0
Behavior4/5

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

Annotations already indicate readOnlyHint=true and destructiveHint=false. The description adds behavioral context: the key parameter uses dot-separated wildcard filtering, the result can be large when no key is given, and the return includes decoded series with labels and per-period values. No contradiction.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is concise, with two paragraphs and an example. It front-loads the main purpose and efficiently covers key usage details without excess. Every sentence adds value.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness4/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given no output schema, the description adequately states what is returned (decoded series with dimension labels and per-period values). It covers prerequisites, parameter behavior, and a caution. Slightly more detail on response structure could be helpful, but sufficient for a read-only tool.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters5/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema coverage is 100% (all parameters described), baseline 3. The description adds substantial meaning beyond the schema: it explains the dot-separated SDMX filter format, the positional wildcard behavior, and provides an explicit example. This significantly aids correct usage.

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?

The description clearly states 'Pull observations from a UNICEF dataset' specifying a specific verb ('pull') and resource ('UNICEF dataset'). It distinguishes itself from sibling tools like dataflow_structure by noting the prerequisite call.

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?

The description advises calling dataflow_structure first to learn dimension order and valid codes, providing clear usage context. It also warns that omitting the key parameter can return a large dataset. However, it does not explicitly state when not to use this tool or list alternatives.

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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TDQS

A3.5/5.0
Disambiguation2/5

Several tool families heavily overlap: ask_pipeworx, ask_pipeworx_beta, ask_pipeworx_grounded, deep_research, discover_tools, and suggest_questions all serve 'find/query Pipeworx data' with blurry boundaries, and the six Polymarket tools (bet_research, polymarket_edges, polymarket_edge_tracker, polymarket_arbitrage, polymarket_fill_risk, polymarket_kalshi_spread) have overlapping purposes. An agent could easily pick the wrong one without reading every description.

Naming Consistency3/5

Most tools follow snake_case verb_noun patterns (list_dataflows, get_data, compare_entities, resolve_entity), and families share prefixes (pipeworx_*, polymarket_*, ask_pipeworx_*). However, the server is named 'Unicef' while almost all tool names reference Pipeworx/Polymarket, and verb choices vary widely, so the overall set lacks a unified naming story.

Tool Count1/5

34 tools is already on the high side, but the real problem is scope: only 3 tools (list_dataflows, dataflow_structure, get_data) relate to the server's stated UNICEF purpose, while the other 31 are an unrelated grab bag of Pipeworx research, prediction-market betting, memory utilities, npm scanning, and llms.txt generation. This is a severe mismatch between count and purpose.

Completeness2/5

For the UNICEF domain implied by the server name, the surface is minimal: browse, structure, and fetch data cover read-only access but nothing else, and the overwhelming majority of tools are off-domain. If the inferred domain is instead 'Pipeworx + prediction markets', coverage is broad, but then the server name is fundamentally misleading and the UNICEF subset is an incomplete afterthought.

Resources